| 1 | //===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===// |
| 2 | // |
| 3 | // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| 4 | // See https://llvm.org/LICENSE.txt for license information. |
| 5 | // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| 6 | // |
| 7 | //===----------------------------------------------------------------------===// |
| 8 | // |
| 9 | // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops |
| 10 | // and generates target-independent LLVM-IR. |
| 11 | // The vectorizer uses the TargetTransformInfo analysis to estimate the costs |
| 12 | // of instructions in order to estimate the profitability of vectorization. |
| 13 | // |
| 14 | // The loop vectorizer combines consecutive loop iterations into a single |
| 15 | // 'wide' iteration. After this transformation the index is incremented |
| 16 | // by the SIMD vector width, and not by one. |
| 17 | // |
| 18 | // This pass has three parts: |
| 19 | // 1. The main loop pass that drives the different parts. |
| 20 | // 2. LoopVectorizationLegality - A unit that checks for the legality |
| 21 | // of the vectorization. |
| 22 | // 3. InnerLoopVectorizer - A unit that performs the actual |
| 23 | // widening of instructions. |
| 24 | // 4. LoopVectorizationCostModel - A unit that checks for the profitability |
| 25 | // of vectorization. It decides on the optimal vector width, which |
| 26 | // can be one, if vectorization is not profitable. |
| 27 | // |
| 28 | // There is a development effort going on to migrate loop vectorizer to the |
| 29 | // VPlan infrastructure and to introduce outer loop vectorization support (see |
| 30 | // docs/Proposal/VectorizationPlan.rst and |
| 31 | // http://lists.llvm.org/pipermail/llvm-dev/2017-December/119523.html). For this |
| 32 | // purpose, we temporarily introduced the VPlan-native vectorization path: an |
| 33 | // alternative vectorization path that is natively implemented on top of the |
| 34 | // VPlan infrastructure. See EnableVPlanNativePath for enabling. |
| 35 | // |
| 36 | //===----------------------------------------------------------------------===// |
| 37 | // |
| 38 | // The reduction-variable vectorization is based on the paper: |
| 39 | // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization. |
| 40 | // |
| 41 | // Variable uniformity checks are inspired by: |
| 42 | // Karrenberg, R. and Hack, S. Whole Function Vectorization. |
| 43 | // |
| 44 | // The interleaved access vectorization is based on the paper: |
| 45 | // Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved |
| 46 | // Data for SIMD |
| 47 | // |
| 48 | // Other ideas/concepts are from: |
| 49 | // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later. |
| 50 | // |
| 51 | // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of |
| 52 | // Vectorizing Compilers. |
| 53 | // |
| 54 | //===----------------------------------------------------------------------===// |
| 55 | |
| 56 | #include "llvm/Transforms/Vectorize/LoopVectorize.h" |
| 57 | #include "LoopVectorizationPlanner.h" |
| 58 | #include "VPRecipeBuilder.h" |
| 59 | #include "VPlan.h" |
| 60 | #include "VPlanHCFGBuilder.h" |
| 61 | #include "VPlanPredicator.h" |
| 62 | #include "VPlanTransforms.h" |
| 63 | #include "llvm/ADT/APInt.h" |
| 64 | #include "llvm/ADT/ArrayRef.h" |
| 65 | #include "llvm/ADT/DenseMap.h" |
| 66 | #include "llvm/ADT/DenseMapInfo.h" |
| 67 | #include "llvm/ADT/Hashing.h" |
| 68 | #include "llvm/ADT/MapVector.h" |
| 69 | #include "llvm/ADT/None.h" |
| 70 | #include "llvm/ADT/Optional.h" |
| 71 | #include "llvm/ADT/STLExtras.h" |
| 72 | #include "llvm/ADT/SetVector.h" |
| 73 | #include "llvm/ADT/SmallPtrSet.h" |
| 74 | #include "llvm/ADT/SmallVector.h" |
| 75 | #include "llvm/ADT/Statistic.h" |
| 76 | #include "llvm/ADT/StringRef.h" |
| 77 | #include "llvm/ADT/Twine.h" |
| 78 | #include "llvm/ADT/iterator_range.h" |
| 79 | #include "llvm/Analysis/AssumptionCache.h" |
| 80 | #include "llvm/Analysis/BasicAliasAnalysis.h" |
| 81 | #include "llvm/Analysis/BlockFrequencyInfo.h" |
| 82 | #include "llvm/Analysis/CFG.h" |
| 83 | #include "llvm/Analysis/CodeMetrics.h" |
| 84 | #include "llvm/Analysis/DemandedBits.h" |
| 85 | #include "llvm/Analysis/GlobalsModRef.h" |
| 86 | #include "llvm/Analysis/LoopAccessAnalysis.h" |
| 87 | #include "llvm/Analysis/LoopAnalysisManager.h" |
| 88 | #include "llvm/Analysis/LoopInfo.h" |
| 89 | #include "llvm/Analysis/LoopIterator.h" |
| 90 | #include "llvm/Analysis/MemorySSA.h" |
| 91 | #include "llvm/Analysis/OptimizationRemarkEmitter.h" |
| 92 | #include "llvm/Analysis/ProfileSummaryInfo.h" |
| 93 | #include "llvm/Analysis/ScalarEvolution.h" |
| 94 | #include "llvm/Analysis/ScalarEvolutionExpressions.h" |
| 95 | #include "llvm/Analysis/TargetLibraryInfo.h" |
| 96 | #include "llvm/Analysis/TargetTransformInfo.h" |
| 97 | #include "llvm/Analysis/VectorUtils.h" |
| 98 | #include "llvm/IR/Attributes.h" |
| 99 | #include "llvm/IR/BasicBlock.h" |
| 100 | #include "llvm/IR/CFG.h" |
| 101 | #include "llvm/IR/Constant.h" |
| 102 | #include "llvm/IR/Constants.h" |
| 103 | #include "llvm/IR/DataLayout.h" |
| 104 | #include "llvm/IR/DebugInfoMetadata.h" |
| 105 | #include "llvm/IR/DebugLoc.h" |
| 106 | #include "llvm/IR/DerivedTypes.h" |
| 107 | #include "llvm/IR/DiagnosticInfo.h" |
| 108 | #include "llvm/IR/Dominators.h" |
| 109 | #include "llvm/IR/Function.h" |
| 110 | #include "llvm/IR/IRBuilder.h" |
| 111 | #include "llvm/IR/InstrTypes.h" |
| 112 | #include "llvm/IR/Instruction.h" |
| 113 | #include "llvm/IR/Instructions.h" |
| 114 | #include "llvm/IR/IntrinsicInst.h" |
| 115 | #include "llvm/IR/Intrinsics.h" |
| 116 | #include "llvm/IR/LLVMContext.h" |
| 117 | #include "llvm/IR/Metadata.h" |
| 118 | #include "llvm/IR/Module.h" |
| 119 | #include "llvm/IR/Operator.h" |
| 120 | #include "llvm/IR/Type.h" |
| 121 | #include "llvm/IR/Use.h" |
| 122 | #include "llvm/IR/User.h" |
| 123 | #include "llvm/IR/Value.h" |
| 124 | #include "llvm/IR/ValueHandle.h" |
| 125 | #include "llvm/IR/Verifier.h" |
| 126 | #include "llvm/InitializePasses.h" |
| 127 | #include "llvm/Pass.h" |
| 128 | #include "llvm/Support/Casting.h" |
| 129 | #include "llvm/Support/CommandLine.h" |
| 130 | #include "llvm/Support/Compiler.h" |
| 131 | #include "llvm/Support/Debug.h" |
| 132 | #include "llvm/Support/ErrorHandling.h" |
| 133 | #include "llvm/Support/InstructionCost.h" |
| 134 | #include "llvm/Support/MathExtras.h" |
| 135 | #include "llvm/Support/raw_ostream.h" |
| 136 | #include "llvm/Transforms/Utils/BasicBlockUtils.h" |
| 137 | #include "llvm/Transforms/Utils/InjectTLIMappings.h" |
| 138 | #include "llvm/Transforms/Utils/LoopSimplify.h" |
| 139 | #include "llvm/Transforms/Utils/LoopUtils.h" |
| 140 | #include "llvm/Transforms/Utils/LoopVersioning.h" |
| 141 | #include "llvm/Transforms/Utils/ScalarEvolutionExpander.h" |
| 142 | #include "llvm/Transforms/Utils/SizeOpts.h" |
| 143 | #include "llvm/Transforms/Vectorize/LoopVectorizationLegality.h" |
| 144 | #include <algorithm> |
| 145 | #include <cassert> |
| 146 | #include <cstdint> |
| 147 | #include <cstdlib> |
| 148 | #include <functional> |
| 149 | #include <iterator> |
| 150 | #include <limits> |
| 151 | #include <memory> |
| 152 | #include <string> |
| 153 | #include <tuple> |
| 154 | #include <utility> |
| 155 | |
| 156 | using namespace llvm; |
| 157 | |
| 158 | #define LV_NAME "loop-vectorize" |
| 159 | #define DEBUG_TYPE LV_NAME |
| 160 | |
| 161 | #ifndef NDEBUG |
| 162 | const char VerboseDebug[] = DEBUG_TYPE "-verbose" ; |
| 163 | #endif |
| 164 | |
| 165 | /// @{ |
| 166 | /// Metadata attribute names |
| 167 | const char LLVMLoopVectorizeFollowupAll[] = "llvm.loop.vectorize.followup_all" ; |
| 168 | const char LLVMLoopVectorizeFollowupVectorized[] = |
| 169 | "llvm.loop.vectorize.followup_vectorized" ; |
| 170 | const char LLVMLoopVectorizeFollowupEpilogue[] = |
| 171 | "llvm.loop.vectorize.followup_epilogue" ; |
| 172 | /// @} |
| 173 | |
| 174 | STATISTIC(LoopsVectorized, "Number of loops vectorized" ); |
| 175 | STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization" ); |
| 176 | STATISTIC(LoopsEpilogueVectorized, "Number of epilogues vectorized" ); |
| 177 | |
| 178 | static cl::opt<bool> EnableEpilogueVectorization( |
| 179 | "enable-epilogue-vectorization" , cl::init(true), cl::Hidden, |
| 180 | cl::desc("Enable vectorization of epilogue loops." )); |
| 181 | |
| 182 | static cl::opt<unsigned> EpilogueVectorizationForceVF( |
| 183 | "epilogue-vectorization-force-VF" , cl::init(1), cl::Hidden, |
| 184 | cl::desc("When epilogue vectorization is enabled, and a value greater than " |
| 185 | "1 is specified, forces the given VF for all applicable epilogue " |
| 186 | "loops." )); |
| 187 | |
| 188 | static cl::opt<unsigned> EpilogueVectorizationMinVF( |
| 189 | "epilogue-vectorization-minimum-VF" , cl::init(16), cl::Hidden, |
| 190 | cl::desc("Only loops with vectorization factor equal to or larger than " |
| 191 | "the specified value are considered for epilogue vectorization." )); |
| 192 | |
| 193 | /// Loops with a known constant trip count below this number are vectorized only |
| 194 | /// if no scalar iteration overheads are incurred. |
| 195 | static cl::opt<unsigned> TinyTripCountVectorThreshold( |
| 196 | "vectorizer-min-trip-count" , cl::init(16), cl::Hidden, |
| 197 | cl::desc("Loops with a constant trip count that is smaller than this " |
| 198 | "value are vectorized only if no scalar iteration overheads " |
| 199 | "are incurred." )); |
| 200 | |
| 201 | // Option prefer-predicate-over-epilogue indicates that an epilogue is undesired, |
| 202 | // that predication is preferred, and this lists all options. I.e., the |
| 203 | // vectorizer will try to fold the tail-loop (epilogue) into the vector body |
| 204 | // and predicate the instructions accordingly. If tail-folding fails, there are |
| 205 | // different fallback strategies depending on these values: |
| 206 | namespace PreferPredicateTy { |
| 207 | enum Option { |
| 208 | ScalarEpilogue = 0, |
| 209 | PredicateElseScalarEpilogue, |
| 210 | PredicateOrDontVectorize |
| 211 | }; |
| 212 | } // namespace PreferPredicateTy |
| 213 | |
| 214 | static cl::opt<PreferPredicateTy::Option> PreferPredicateOverEpilogue( |
| 215 | "prefer-predicate-over-epilogue" , |
| 216 | cl::init(PreferPredicateTy::ScalarEpilogue), |
| 217 | cl::Hidden, |
| 218 | cl::desc("Tail-folding and predication preferences over creating a scalar " |
| 219 | "epilogue loop." ), |
| 220 | cl::values(clEnumValN(PreferPredicateTy::ScalarEpilogue, |
| 221 | "scalar-epilogue" , |
| 222 | "Don't tail-predicate loops, create scalar epilogue" ), |
| 223 | clEnumValN(PreferPredicateTy::PredicateElseScalarEpilogue, |
| 224 | "predicate-else-scalar-epilogue" , |
| 225 | "prefer tail-folding, create scalar epilogue if tail " |
| 226 | "folding fails." ), |
| 227 | clEnumValN(PreferPredicateTy::PredicateOrDontVectorize, |
| 228 | "predicate-dont-vectorize" , |
| 229 | "prefers tail-folding, don't attempt vectorization if " |
| 230 | "tail-folding fails." ))); |
| 231 | |
| 232 | static cl::opt<bool> MaximizeBandwidth( |
| 233 | "vectorizer-maximize-bandwidth" , cl::init(false), cl::Hidden, |
| 234 | cl::desc("Maximize bandwidth when selecting vectorization factor which " |
| 235 | "will be determined by the smallest type in loop." )); |
| 236 | |
| 237 | static cl::opt<bool> EnableInterleavedMemAccesses( |
| 238 | "enable-interleaved-mem-accesses" , cl::init(false), cl::Hidden, |
| 239 | cl::desc("Enable vectorization on interleaved memory accesses in a loop" )); |
| 240 | |
| 241 | /// An interleave-group may need masking if it resides in a block that needs |
| 242 | /// predication, or in order to mask away gaps. |
| 243 | static cl::opt<bool> EnableMaskedInterleavedMemAccesses( |
| 244 | "enable-masked-interleaved-mem-accesses" , cl::init(false), cl::Hidden, |
| 245 | cl::desc("Enable vectorization on masked interleaved memory accesses in a loop" )); |
| 246 | |
| 247 | static cl::opt<unsigned> TinyTripCountInterleaveThreshold( |
| 248 | "tiny-trip-count-interleave-threshold" , cl::init(128), cl::Hidden, |
| 249 | cl::desc("We don't interleave loops with a estimated constant trip count " |
| 250 | "below this number" )); |
| 251 | |
| 252 | static cl::opt<unsigned> ForceTargetNumScalarRegs( |
| 253 | "force-target-num-scalar-regs" , cl::init(0), cl::Hidden, |
| 254 | cl::desc("A flag that overrides the target's number of scalar registers." )); |
| 255 | |
| 256 | static cl::opt<unsigned> ForceTargetNumVectorRegs( |
| 257 | "force-target-num-vector-regs" , cl::init(0), cl::Hidden, |
| 258 | cl::desc("A flag that overrides the target's number of vector registers." )); |
| 259 | |
| 260 | static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor( |
| 261 | "force-target-max-scalar-interleave" , cl::init(0), cl::Hidden, |
| 262 | cl::desc("A flag that overrides the target's max interleave factor for " |
| 263 | "scalar loops." )); |
| 264 | |
| 265 | static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor( |
| 266 | "force-target-max-vector-interleave" , cl::init(0), cl::Hidden, |
| 267 | cl::desc("A flag that overrides the target's max interleave factor for " |
| 268 | "vectorized loops." )); |
| 269 | |
| 270 | static cl::opt<unsigned> ForceTargetInstructionCost( |
| 271 | "force-target-instruction-cost" , cl::init(0), cl::Hidden, |
| 272 | cl::desc("A flag that overrides the target's expected cost for " |
| 273 | "an instruction to a single constant value. Mostly " |
| 274 | "useful for getting consistent testing." )); |
| 275 | |
| 276 | static cl::opt<bool> ForceTargetSupportsScalableVectors( |
| 277 | "force-target-supports-scalable-vectors" , cl::init(false), cl::Hidden, |
| 278 | cl::desc( |
| 279 | "Pretend that scalable vectors are supported, even if the target does " |
| 280 | "not support them. This flag should only be used for testing." )); |
| 281 | |
| 282 | static cl::opt<unsigned> SmallLoopCost( |
| 283 | "small-loop-cost" , cl::init(20), cl::Hidden, |
| 284 | cl::desc( |
| 285 | "The cost of a loop that is considered 'small' by the interleaver." )); |
| 286 | |
| 287 | static cl::opt<bool> LoopVectorizeWithBlockFrequency( |
| 288 | "loop-vectorize-with-block-frequency" , cl::init(true), cl::Hidden, |
| 289 | cl::desc("Enable the use of the block frequency analysis to access PGO " |
| 290 | "heuristics minimizing code growth in cold regions and being more " |
| 291 | "aggressive in hot regions." )); |
| 292 | |
| 293 | // Runtime interleave loops for load/store throughput. |
| 294 | static cl::opt<bool> EnableLoadStoreRuntimeInterleave( |
| 295 | "enable-loadstore-runtime-interleave" , cl::init(true), cl::Hidden, |
| 296 | cl::desc( |
| 297 | "Enable runtime interleaving until load/store ports are saturated" )); |
| 298 | |
| 299 | /// Interleave small loops with scalar reductions. |
| 300 | static cl::opt<bool> InterleaveSmallLoopScalarReduction( |
| 301 | "interleave-small-loop-scalar-reduction" , cl::init(false), cl::Hidden, |
| 302 | cl::desc("Enable interleaving for loops with small iteration counts that " |
| 303 | "contain scalar reductions to expose ILP." )); |
| 304 | |
| 305 | /// The number of stores in a loop that are allowed to need predication. |
| 306 | static cl::opt<unsigned> NumberOfStoresToPredicate( |
| 307 | "vectorize-num-stores-pred" , cl::init(1), cl::Hidden, |
| 308 | cl::desc("Max number of stores to be predicated behind an if." )); |
| 309 | |
| 310 | static cl::opt<bool> EnableIndVarRegisterHeur( |
| 311 | "enable-ind-var-reg-heur" , cl::init(true), cl::Hidden, |
| 312 | cl::desc("Count the induction variable only once when interleaving" )); |
| 313 | |
| 314 | static cl::opt<bool> EnableCondStoresVectorization( |
| 315 | "enable-cond-stores-vec" , cl::init(true), cl::Hidden, |
| 316 | cl::desc("Enable if predication of stores during vectorization." )); |
| 317 | |
| 318 | static cl::opt<unsigned> MaxNestedScalarReductionIC( |
| 319 | "max-nested-scalar-reduction-interleave" , cl::init(2), cl::Hidden, |
| 320 | cl::desc("The maximum interleave count to use when interleaving a scalar " |
| 321 | "reduction in a nested loop." )); |
| 322 | |
| 323 | static cl::opt<bool> |
| 324 | PreferInLoopReductions("prefer-inloop-reductions" , cl::init(false), |
| 325 | cl::Hidden, |
| 326 | cl::desc("Prefer in-loop vector reductions, " |
| 327 | "overriding the targets preference." )); |
| 328 | |
| 329 | static cl::opt<bool> PreferPredicatedReductionSelect( |
| 330 | "prefer-predicated-reduction-select" , cl::init(false), cl::Hidden, |
| 331 | cl::desc( |
| 332 | "Prefer predicating a reduction operation over an after loop select." )); |
| 333 | |
| 334 | cl::opt<bool> EnableVPlanNativePath( |
| 335 | "enable-vplan-native-path" , cl::init(false), cl::Hidden, |
| 336 | cl::desc("Enable VPlan-native vectorization path with " |
| 337 | "support for outer loop vectorization." )); |
| 338 | |
| 339 | // FIXME: Remove this switch once we have divergence analysis. Currently we |
| 340 | // assume divergent non-backedge branches when this switch is true. |
| 341 | cl::opt<bool> EnableVPlanPredication( |
| 342 | "enable-vplan-predication" , cl::init(false), cl::Hidden, |
| 343 | cl::desc("Enable VPlan-native vectorization path predicator with " |
| 344 | "support for outer loop vectorization." )); |
| 345 | |
| 346 | // This flag enables the stress testing of the VPlan H-CFG construction in the |
| 347 | // VPlan-native vectorization path. It must be used in conjuction with |
| 348 | // -enable-vplan-native-path. -vplan-verify-hcfg can also be used to enable the |
| 349 | // verification of the H-CFGs built. |
| 350 | static cl::opt<bool> VPlanBuildStressTest( |
| 351 | "vplan-build-stress-test" , cl::init(false), cl::Hidden, |
| 352 | cl::desc( |
| 353 | "Build VPlan for every supported loop nest in the function and bail " |
| 354 | "out right after the build (stress test the VPlan H-CFG construction " |
| 355 | "in the VPlan-native vectorization path)." )); |
| 356 | |
| 357 | cl::opt<bool> llvm::EnableLoopInterleaving( |
| 358 | "interleave-loops" , cl::init(true), cl::Hidden, |
| 359 | cl::desc("Enable loop interleaving in Loop vectorization passes" )); |
| 360 | cl::opt<bool> llvm::EnableLoopVectorization( |
| 361 | "vectorize-loops" , cl::init(true), cl::Hidden, |
| 362 | cl::desc("Run the Loop vectorization passes" )); |
| 363 | |
| 364 | /// A helper function that returns the type of loaded or stored value. |
| 365 | static Type *getMemInstValueType(Value *I) { |
| 366 | assert((isa<LoadInst>(I) || isa<StoreInst>(I)) && |
| 367 | "Expected Load or Store instruction" ); |
| 368 | if (auto *LI = dyn_cast<LoadInst>(I)) |
| 369 | return LI->getType(); |
| 370 | return cast<StoreInst>(I)->getValueOperand()->getType(); |
| 371 | } |
| 372 | |
| 373 | /// A helper function that returns true if the given type is irregular. The |
| 374 | /// type is irregular if its allocated size doesn't equal the store size of an |
| 375 | /// element of the corresponding vector type. |
| 376 | static bool hasIrregularType(Type *Ty, const DataLayout &DL) { |
| 377 | // Determine if an array of N elements of type Ty is "bitcast compatible" |
| 378 | // with a <N x Ty> vector. |
| 379 | // This is only true if there is no padding between the array elements. |
| 380 | return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty); |
| 381 | } |
| 382 | |
| 383 | /// A helper function that returns the reciprocal of the block probability of |
| 384 | /// predicated blocks. If we return X, we are assuming the predicated block |
| 385 | /// will execute once for every X iterations of the loop header. |
| 386 | /// |
| 387 | /// TODO: We should use actual block probability here, if available. Currently, |
| 388 | /// we always assume predicated blocks have a 50% chance of executing. |
| 389 | static unsigned getReciprocalPredBlockProb() { return 2; } |
| 390 | |
| 391 | /// A helper function that adds a 'fast' flag to floating-point operations. |
| 392 | static Value *addFastMathFlag(Value *V) { |
| 393 | if (isa<FPMathOperator>(V)) |
| 394 | cast<Instruction>(V)->setFastMathFlags(FastMathFlags::getFast()); |
| 395 | return V; |
| 396 | } |
| 397 | |
| 398 | static Value *addFastMathFlag(Value *V, FastMathFlags FMF) { |
| 399 | if (isa<FPMathOperator>(V)) |
| 400 | cast<Instruction>(V)->setFastMathFlags(FMF); |
| 401 | return V; |
| 402 | } |
| 403 | |
| 404 | /// A helper function that returns an integer or floating-point constant with |
| 405 | /// value C. |
| 406 | static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) { |
| 407 | return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C) |
| 408 | : ConstantFP::get(Ty, C); |
| 409 | } |
| 410 | |
| 411 | /// Returns "best known" trip count for the specified loop \p L as defined by |
| 412 | /// the following procedure: |
| 413 | /// 1) Returns exact trip count if it is known. |
| 414 | /// 2) Returns expected trip count according to profile data if any. |
| 415 | /// 3) Returns upper bound estimate if it is known. |
| 416 | /// 4) Returns None if all of the above failed. |
| 417 | static Optional<unsigned> getSmallBestKnownTC(ScalarEvolution &SE, Loop *L) { |
| 418 | // Check if exact trip count is known. |
| 419 | if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L)) |
| 420 | return ExpectedTC; |
| 421 | |
| 422 | // Check if there is an expected trip count available from profile data. |
| 423 | if (LoopVectorizeWithBlockFrequency) |
| 424 | if (auto EstimatedTC = getLoopEstimatedTripCount(L)) |
| 425 | return EstimatedTC; |
| 426 | |
| 427 | // Check if upper bound estimate is known. |
| 428 | if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L)) |
| 429 | return ExpectedTC; |
| 430 | |
| 431 | return None; |
| 432 | } |
| 433 | |
| 434 | namespace llvm { |
| 435 | |
| 436 | /// InnerLoopVectorizer vectorizes loops which contain only one basic |
| 437 | /// block to a specified vectorization factor (VF). |
| 438 | /// This class performs the widening of scalars into vectors, or multiple |
| 439 | /// scalars. This class also implements the following features: |
| 440 | /// * It inserts an epilogue loop for handling loops that don't have iteration |
| 441 | /// counts that are known to be a multiple of the vectorization factor. |
| 442 | /// * It handles the code generation for reduction variables. |
| 443 | /// * Scalarization (implementation using scalars) of un-vectorizable |
| 444 | /// instructions. |
| 445 | /// InnerLoopVectorizer does not perform any vectorization-legality |
| 446 | /// checks, and relies on the caller to check for the different legality |
| 447 | /// aspects. The InnerLoopVectorizer relies on the |
| 448 | /// LoopVectorizationLegality class to provide information about the induction |
| 449 | /// and reduction variables that were found to a given vectorization factor. |
| 450 | class InnerLoopVectorizer { |
| 451 | public: |
| 452 | InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| 453 | LoopInfo *LI, DominatorTree *DT, |
| 454 | const TargetLibraryInfo *TLI, |
| 455 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 456 | OptimizationRemarkEmitter *ORE, ElementCount VecWidth, |
| 457 | unsigned UnrollFactor, LoopVectorizationLegality *LVL, |
| 458 | LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, |
| 459 | ProfileSummaryInfo *PSI) |
| 460 | : OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI), |
| 461 | AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor), |
| 462 | Builder(PSE.getSE()->getContext()), |
| 463 | VectorLoopValueMap(UnrollFactor, VecWidth), Legal(LVL), Cost(CM), |
| 464 | BFI(BFI), PSI(PSI) { |
| 465 | // Query this against the original loop and save it here because the profile |
| 466 | // of the original loop header may change as the transformation happens. |
| 467 | OptForSizeBasedOnProfile = llvm::shouldOptimizeForSize( |
| 468 | OrigLoop->getHeader(), PSI, BFI, PGSOQueryType::IRPass); |
| 469 | } |
| 470 | |
| 471 | virtual ~InnerLoopVectorizer() = default; |
| 472 | |
| 473 | /// Create a new empty loop that will contain vectorized instructions later |
| 474 | /// on, while the old loop will be used as the scalar remainder. Control flow |
| 475 | /// is generated around the vectorized (and scalar epilogue) loops consisting |
| 476 | /// of various checks and bypasses. Return the pre-header block of the new |
| 477 | /// loop. |
| 478 | /// In the case of epilogue vectorization, this function is overriden to |
| 479 | /// handle the more complex control flow around the loops. |
| 480 | virtual BasicBlock *createVectorizedLoopSkeleton(); |
| 481 | |
| 482 | /// Widen a single instruction within the innermost loop. |
| 483 | void widenInstruction(Instruction &I, VPValue *Def, VPUser &Operands, |
| 484 | VPTransformState &State); |
| 485 | |
| 486 | /// Widen a single call instruction within the innermost loop. |
| 487 | void widenCallInstruction(CallInst &I, VPValue *Def, VPUser &ArgOperands, |
| 488 | VPTransformState &State); |
| 489 | |
| 490 | /// Widen a single select instruction within the innermost loop. |
| 491 | void widenSelectInstruction(SelectInst &I, VPValue *VPDef, VPUser &Operands, |
| 492 | bool InvariantCond, VPTransformState &State); |
| 493 | |
| 494 | /// Fix the vectorized code, taking care of header phi's, live-outs, and more. |
| 495 | void fixVectorizedLoop(); |
| 496 | |
| 497 | // Return true if any runtime check is added. |
| 498 | bool areSafetyChecksAdded() { return AddedSafetyChecks; } |
| 499 | |
| 500 | /// A type for vectorized values in the new loop. Each value from the |
| 501 | /// original loop, when vectorized, is represented by UF vector values in the |
| 502 | /// new unrolled loop, where UF is the unroll factor. |
| 503 | using VectorParts = SmallVector<Value *, 2>; |
| 504 | |
| 505 | /// Vectorize a single GetElementPtrInst based on information gathered and |
| 506 | /// decisions taken during planning. |
| 507 | void widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, VPUser &Indices, |
| 508 | unsigned UF, ElementCount VF, bool IsPtrLoopInvariant, |
| 509 | SmallBitVector &IsIndexLoopInvariant, VPTransformState &State); |
| 510 | |
| 511 | /// Vectorize a single PHINode in a block. This method handles the induction |
| 512 | /// variable canonicalization. It supports both VF = 1 for unrolled loops and |
| 513 | /// arbitrary length vectors. |
| 514 | void widenPHIInstruction(Instruction *PN, RecurrenceDescriptor *RdxDesc, |
| 515 | Value *StartV, unsigned UF, ElementCount VF); |
| 516 | |
| 517 | /// A helper function to scalarize a single Instruction in the innermost loop. |
| 518 | /// Generates a sequence of scalar instances for each lane between \p MinLane |
| 519 | /// and \p MaxLane, times each part between \p MinPart and \p MaxPart, |
| 520 | /// inclusive. Uses the VPValue operands from \p Operands instead of \p |
| 521 | /// Instr's operands. |
| 522 | void scalarizeInstruction(Instruction *Instr, VPUser &Operands, |
| 523 | const VPIteration &Instance, bool IfPredicateInstr, |
| 524 | VPTransformState &State); |
| 525 | |
| 526 | /// Widen an integer or floating-point induction variable \p IV. If \p Trunc |
| 527 | /// is provided, the integer induction variable will first be truncated to |
| 528 | /// the corresponding type. |
| 529 | void widenIntOrFpInduction(PHINode *IV, Value *Start, |
| 530 | TruncInst *Trunc = nullptr); |
| 531 | |
| 532 | /// getOrCreateVectorValue and getOrCreateScalarValue coordinate to generate a |
| 533 | /// vector or scalar value on-demand if one is not yet available. When |
| 534 | /// vectorizing a loop, we visit the definition of an instruction before its |
| 535 | /// uses. When visiting the definition, we either vectorize or scalarize the |
| 536 | /// instruction, creating an entry for it in the corresponding map. (In some |
| 537 | /// cases, such as induction variables, we will create both vector and scalar |
| 538 | /// entries.) Then, as we encounter uses of the definition, we derive values |
| 539 | /// for each scalar or vector use unless such a value is already available. |
| 540 | /// For example, if we scalarize a definition and one of its uses is vector, |
| 541 | /// we build the required vector on-demand with an insertelement sequence |
| 542 | /// when visiting the use. Otherwise, if the use is scalar, we can use the |
| 543 | /// existing scalar definition. |
| 544 | /// |
| 545 | /// Return a value in the new loop corresponding to \p V from the original |
| 546 | /// loop at unroll index \p Part. If the value has already been vectorized, |
| 547 | /// the corresponding vector entry in VectorLoopValueMap is returned. If, |
| 548 | /// however, the value has a scalar entry in VectorLoopValueMap, we construct |
| 549 | /// a new vector value on-demand by inserting the scalar values into a vector |
| 550 | /// with an insertelement sequence. If the value has been neither vectorized |
| 551 | /// nor scalarized, it must be loop invariant, so we simply broadcast the |
| 552 | /// value into a vector. |
| 553 | Value *getOrCreateVectorValue(Value *V, unsigned Part); |
| 554 | |
| 555 | void setVectorValue(Value *Scalar, unsigned Part, Value *Vector) { |
| 556 | VectorLoopValueMap.setVectorValue(Scalar, Part, Vector); |
| 557 | } |
| 558 | |
| 559 | /// Return a value in the new loop corresponding to \p V from the original |
| 560 | /// loop at unroll and vector indices \p Instance. If the value has been |
| 561 | /// vectorized but not scalarized, the necessary extractelement instruction |
| 562 | /// will be generated. |
| 563 | Value *getOrCreateScalarValue(Value *V, const VPIteration &Instance); |
| 564 | |
| 565 | /// Construct the vector value of a scalarized value \p V one lane at a time. |
| 566 | void packScalarIntoVectorValue(Value *V, const VPIteration &Instance); |
| 567 | |
| 568 | /// Try to vectorize interleaved access group \p Group with the base address |
| 569 | /// given in \p Addr, optionally masking the vector operations if \p |
| 570 | /// BlockInMask is non-null. Use \p State to translate given VPValues to IR |
| 571 | /// values in the vectorized loop. |
| 572 | void vectorizeInterleaveGroup(const InterleaveGroup<Instruction> *Group, |
| 573 | ArrayRef<VPValue *> VPDefs, |
| 574 | VPTransformState &State, VPValue *Addr, |
| 575 | ArrayRef<VPValue *> StoredValues, |
| 576 | VPValue *BlockInMask = nullptr); |
| 577 | |
| 578 | /// Vectorize Load and Store instructions with the base address given in \p |
| 579 | /// Addr, optionally masking the vector operations if \p BlockInMask is |
| 580 | /// non-null. Use \p State to translate given VPValues to IR values in the |
| 581 | /// vectorized loop. |
| 582 | void vectorizeMemoryInstruction(Instruction *Instr, VPTransformState &State, |
| 583 | VPValue *Def, VPValue *Addr, |
| 584 | VPValue *StoredValue, VPValue *BlockInMask); |
| 585 | |
| 586 | /// Set the debug location in the builder using the debug location in |
| 587 | /// the instruction. |
| 588 | void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr); |
| 589 | |
| 590 | /// Fix the non-induction PHIs in the OrigPHIsToFix vector. |
| 591 | void fixNonInductionPHIs(void); |
| 592 | |
| 593 | protected: |
| 594 | friend class LoopVectorizationPlanner; |
| 595 | |
| 596 | /// A small list of PHINodes. |
| 597 | using PhiVector = SmallVector<PHINode *, 4>; |
| 598 | |
| 599 | /// A type for scalarized values in the new loop. Each value from the |
| 600 | /// original loop, when scalarized, is represented by UF x VF scalar values |
| 601 | /// in the new unrolled loop, where UF is the unroll factor and VF is the |
| 602 | /// vectorization factor. |
| 603 | using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>; |
| 604 | |
| 605 | /// Set up the values of the IVs correctly when exiting the vector loop. |
| 606 | void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II, |
| 607 | Value *CountRoundDown, Value *EndValue, |
| 608 | BasicBlock *MiddleBlock); |
| 609 | |
| 610 | /// Create a new induction variable inside L. |
| 611 | PHINode *createInductionVariable(Loop *L, Value *Start, Value *End, |
| 612 | Value *Step, Instruction *DL); |
| 613 | |
| 614 | /// Handle all cross-iteration phis in the header. |
| 615 | void fixCrossIterationPHIs(); |
| 616 | |
| 617 | /// Fix a first-order recurrence. This is the second phase of vectorizing |
| 618 | /// this phi node. |
| 619 | void fixFirstOrderRecurrence(PHINode *Phi); |
| 620 | |
| 621 | /// Fix a reduction cross-iteration phi. This is the second phase of |
| 622 | /// vectorizing this phi node. |
| 623 | void fixReduction(PHINode *Phi); |
| 624 | |
| 625 | /// Clear NSW/NUW flags from reduction instructions if necessary. |
| 626 | void clearReductionWrapFlags(RecurrenceDescriptor &RdxDesc); |
| 627 | |
| 628 | /// Fixup the LCSSA phi nodes in the unique exit block. This simply |
| 629 | /// means we need to add the appropriate incoming value from the middle |
| 630 | /// block as exiting edges from the scalar epilogue loop (if present) are |
| 631 | /// already in place, and we exit the vector loop exclusively to the middle |
| 632 | /// block. |
| 633 | void fixLCSSAPHIs(); |
| 634 | |
| 635 | /// Iteratively sink the scalarized operands of a predicated instruction into |
| 636 | /// the block that was created for it. |
| 637 | void sinkScalarOperands(Instruction *PredInst); |
| 638 | |
| 639 | /// Shrinks vector element sizes to the smallest bitwidth they can be legally |
| 640 | /// represented as. |
| 641 | void truncateToMinimalBitwidths(); |
| 642 | |
| 643 | /// Create a broadcast instruction. This method generates a broadcast |
| 644 | /// instruction (shuffle) for loop invariant values and for the induction |
| 645 | /// value. If this is the induction variable then we extend it to N, N+1, ... |
| 646 | /// this is needed because each iteration in the loop corresponds to a SIMD |
| 647 | /// element. |
| 648 | virtual Value *getBroadcastInstrs(Value *V); |
| 649 | |
| 650 | /// This function adds (StartIdx, StartIdx + Step, StartIdx + 2*Step, ...) |
| 651 | /// to each vector element of Val. The sequence starts at StartIndex. |
| 652 | /// \p Opcode is relevant for FP induction variable. |
| 653 | virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step, |
| 654 | Instruction::BinaryOps Opcode = |
| 655 | Instruction::BinaryOpsEnd); |
| 656 | |
| 657 | /// Compute scalar induction steps. \p ScalarIV is the scalar induction |
| 658 | /// variable on which to base the steps, \p Step is the size of the step, and |
| 659 | /// \p EntryVal is the value from the original loop that maps to the steps. |
| 660 | /// Note that \p EntryVal doesn't have to be an induction variable - it |
| 661 | /// can also be a truncate instruction. |
| 662 | void buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal, |
| 663 | const InductionDescriptor &ID); |
| 664 | |
| 665 | /// Create a vector induction phi node based on an existing scalar one. \p |
| 666 | /// EntryVal is the value from the original loop that maps to the vector phi |
| 667 | /// node, and \p Step is the loop-invariant step. If \p EntryVal is a |
| 668 | /// truncate instruction, instead of widening the original IV, we widen a |
| 669 | /// version of the IV truncated to \p EntryVal's type. |
| 670 | void createVectorIntOrFpInductionPHI(const InductionDescriptor &II, |
| 671 | Value *Step, Value *Start, |
| 672 | Instruction *EntryVal); |
| 673 | |
| 674 | /// Returns true if an instruction \p I should be scalarized instead of |
| 675 | /// vectorized for the chosen vectorization factor. |
| 676 | bool shouldScalarizeInstruction(Instruction *I) const; |
| 677 | |
| 678 | /// Returns true if we should generate a scalar version of \p IV. |
| 679 | bool needsScalarInduction(Instruction *IV) const; |
| 680 | |
| 681 | /// If there is a cast involved in the induction variable \p ID, which should |
| 682 | /// be ignored in the vectorized loop body, this function records the |
| 683 | /// VectorLoopValue of the respective Phi also as the VectorLoopValue of the |
| 684 | /// cast. We had already proved that the casted Phi is equal to the uncasted |
| 685 | /// Phi in the vectorized loop (under a runtime guard), and therefore |
| 686 | /// there is no need to vectorize the cast - the same value can be used in the |
| 687 | /// vector loop for both the Phi and the cast. |
| 688 | /// If \p VectorLoopValue is a scalarized value, \p Lane is also specified, |
| 689 | /// Otherwise, \p VectorLoopValue is a widened/vectorized value. |
| 690 | /// |
| 691 | /// \p EntryVal is the value from the original loop that maps to the vector |
| 692 | /// phi node and is used to distinguish what is the IV currently being |
| 693 | /// processed - original one (if \p EntryVal is a phi corresponding to the |
| 694 | /// original IV) or the "newly-created" one based on the proof mentioned above |
| 695 | /// (see also buildScalarSteps() and createVectorIntOrFPInductionPHI()). In the |
| 696 | /// latter case \p EntryVal is a TruncInst and we must not record anything for |
| 697 | /// that IV, but it's error-prone to expect callers of this routine to care |
| 698 | /// about that, hence this explicit parameter. |
| 699 | void recordVectorLoopValueForInductionCast(const InductionDescriptor &ID, |
| 700 | const Instruction *EntryVal, |
| 701 | Value *VectorLoopValue, |
| 702 | unsigned Part, |
| 703 | unsigned Lane = UINT_MAX); |
| 704 | |
| 705 | /// Generate a shuffle sequence that will reverse the vector Vec. |
| 706 | virtual Value *reverseVector(Value *Vec); |
| 707 | |
| 708 | /// Returns (and creates if needed) the original loop trip count. |
| 709 | Value *getOrCreateTripCount(Loop *NewLoop); |
| 710 | |
| 711 | /// Returns (and creates if needed) the trip count of the widened loop. |
| 712 | Value *getOrCreateVectorTripCount(Loop *NewLoop); |
| 713 | |
| 714 | /// Returns a bitcasted value to the requested vector type. |
| 715 | /// Also handles bitcasts of vector<float> <-> vector<pointer> types. |
| 716 | Value *createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| 717 | const DataLayout &DL); |
| 718 | |
| 719 | /// Emit a bypass check to see if the vector trip count is zero, including if |
| 720 | /// it overflows. |
| 721 | void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass); |
| 722 | |
| 723 | /// Emit a bypass check to see if all of the SCEV assumptions we've |
| 724 | /// had to make are correct. |
| 725 | void emitSCEVChecks(Loop *L, BasicBlock *Bypass); |
| 726 | |
| 727 | /// Emit bypass checks to check any memory assumptions we may have made. |
| 728 | void emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass); |
| 729 | |
| 730 | /// Compute the transformed value of Index at offset StartValue using step |
| 731 | /// StepValue. |
| 732 | /// For integer induction, returns StartValue + Index * StepValue. |
| 733 | /// For pointer induction, returns StartValue[Index * StepValue]. |
| 734 | /// FIXME: The newly created binary instructions should contain nsw/nuw |
| 735 | /// flags, which can be found from the original scalar operations. |
| 736 | Value *emitTransformedIndex(IRBuilder<> &B, Value *Index, ScalarEvolution *SE, |
| 737 | const DataLayout &DL, |
| 738 | const InductionDescriptor &ID) const; |
| 739 | |
| 740 | /// Emit basic blocks (prefixed with \p Prefix) for the iteration check, |
| 741 | /// vector loop preheader, middle block and scalar preheader. Also |
| 742 | /// allocate a loop object for the new vector loop and return it. |
| 743 | Loop *createVectorLoopSkeleton(StringRef Prefix); |
| 744 | |
| 745 | /// Create new phi nodes for the induction variables to resume iteration count |
| 746 | /// in the scalar epilogue, from where the vectorized loop left off (given by |
| 747 | /// \p VectorTripCount). |
| 748 | /// In cases where the loop skeleton is more complicated (eg. epilogue |
| 749 | /// vectorization) and the resume values can come from an additional bypass |
| 750 | /// block, the \p AdditionalBypass pair provides information about the bypass |
| 751 | /// block and the end value on the edge from bypass to this loop. |
| 752 | void createInductionResumeValues( |
| 753 | Loop *L, Value *VectorTripCount, |
| 754 | std::pair<BasicBlock *, Value *> AdditionalBypass = {nullptr, nullptr}); |
| 755 | |
| 756 | /// Complete the loop skeleton by adding debug MDs, creating appropriate |
| 757 | /// conditional branches in the middle block, preparing the builder and |
| 758 | /// running the verifier. Take in the vector loop \p L as argument, and return |
| 759 | /// the preheader of the completed vector loop. |
| 760 | BasicBlock *completeLoopSkeleton(Loop *L, MDNode *OrigLoopID); |
| 761 | |
| 762 | /// Add additional metadata to \p To that was not present on \p Orig. |
| 763 | /// |
| 764 | /// Currently this is used to add the noalias annotations based on the |
| 765 | /// inserted memchecks. Use this for instructions that are *cloned* into the |
| 766 | /// vector loop. |
| 767 | void addNewMetadata(Instruction *To, const Instruction *Orig); |
| 768 | |
| 769 | /// Add metadata from one instruction to another. |
| 770 | /// |
| 771 | /// This includes both the original MDs from \p From and additional ones (\see |
| 772 | /// addNewMetadata). Use this for *newly created* instructions in the vector |
| 773 | /// loop. |
| 774 | void addMetadata(Instruction *To, Instruction *From); |
| 775 | |
| 776 | /// Similar to the previous function but it adds the metadata to a |
| 777 | /// vector of instructions. |
| 778 | void addMetadata(ArrayRef<Value *> To, Instruction *From); |
| 779 | |
| 780 | /// Allow subclasses to override and print debug traces before/after vplan |
| 781 | /// execution, when trace information is requested. |
| 782 | virtual void printDebugTracesAtStart(){}; |
| 783 | virtual void printDebugTracesAtEnd(){}; |
| 784 | |
| 785 | /// The original loop. |
| 786 | Loop *OrigLoop; |
| 787 | |
| 788 | /// A wrapper around ScalarEvolution used to add runtime SCEV checks. Applies |
| 789 | /// dynamic knowledge to simplify SCEV expressions and converts them to a |
| 790 | /// more usable form. |
| 791 | PredicatedScalarEvolution &PSE; |
| 792 | |
| 793 | /// Loop Info. |
| 794 | LoopInfo *LI; |
| 795 | |
| 796 | /// Dominator Tree. |
| 797 | DominatorTree *DT; |
| 798 | |
| 799 | /// Alias Analysis. |
| 800 | AAResults *AA; |
| 801 | |
| 802 | /// Target Library Info. |
| 803 | const TargetLibraryInfo *TLI; |
| 804 | |
| 805 | /// Target Transform Info. |
| 806 | const TargetTransformInfo *TTI; |
| 807 | |
| 808 | /// Assumption Cache. |
| 809 | AssumptionCache *AC; |
| 810 | |
| 811 | /// Interface to emit optimization remarks. |
| 812 | OptimizationRemarkEmitter *ORE; |
| 813 | |
| 814 | /// LoopVersioning. It's only set up (non-null) if memchecks were |
| 815 | /// used. |
| 816 | /// |
| 817 | /// This is currently only used to add no-alias metadata based on the |
| 818 | /// memchecks. The actually versioning is performed manually. |
| 819 | std::unique_ptr<LoopVersioning> LVer; |
| 820 | |
| 821 | /// The vectorization SIMD factor to use. Each vector will have this many |
| 822 | /// vector elements. |
| 823 | ElementCount VF; |
| 824 | |
| 825 | /// The vectorization unroll factor to use. Each scalar is vectorized to this |
| 826 | /// many different vector instructions. |
| 827 | unsigned UF; |
| 828 | |
| 829 | /// The builder that we use |
| 830 | IRBuilder<> Builder; |
| 831 | |
| 832 | // --- Vectorization state --- |
| 833 | |
| 834 | /// The vector-loop preheader. |
| 835 | BasicBlock *; |
| 836 | |
| 837 | /// The scalar-loop preheader. |
| 838 | BasicBlock *; |
| 839 | |
| 840 | /// Middle Block between the vector and the scalar. |
| 841 | BasicBlock *LoopMiddleBlock; |
| 842 | |
| 843 | /// The (unique) ExitBlock of the scalar loop. Note that |
| 844 | /// there can be multiple exiting edges reaching this block. |
| 845 | BasicBlock *LoopExitBlock; |
| 846 | |
| 847 | /// The vector loop body. |
| 848 | BasicBlock *LoopVectorBody; |
| 849 | |
| 850 | /// The scalar loop body. |
| 851 | BasicBlock *LoopScalarBody; |
| 852 | |
| 853 | /// A list of all bypass blocks. The first block is the entry of the loop. |
| 854 | SmallVector<BasicBlock *, 4> LoopBypassBlocks; |
| 855 | |
| 856 | /// The new Induction variable which was added to the new block. |
| 857 | PHINode *Induction = nullptr; |
| 858 | |
| 859 | /// The induction variable of the old basic block. |
| 860 | PHINode *OldInduction = nullptr; |
| 861 | |
| 862 | /// Maps values from the original loop to their corresponding values in the |
| 863 | /// vectorized loop. A key value can map to either vector values, scalar |
| 864 | /// values or both kinds of values, depending on whether the key was |
| 865 | /// vectorized and scalarized. |
| 866 | VectorizerValueMap VectorLoopValueMap; |
| 867 | |
| 868 | /// Store instructions that were predicated. |
| 869 | SmallVector<Instruction *, 4> PredicatedInstructions; |
| 870 | |
| 871 | /// Trip count of the original loop. |
| 872 | Value *TripCount = nullptr; |
| 873 | |
| 874 | /// Trip count of the widened loop (TripCount - TripCount % (VF*UF)) |
| 875 | Value *VectorTripCount = nullptr; |
| 876 | |
| 877 | /// The legality analysis. |
| 878 | LoopVectorizationLegality *Legal; |
| 879 | |
| 880 | /// The profitablity analysis. |
| 881 | LoopVectorizationCostModel *Cost; |
| 882 | |
| 883 | // Record whether runtime checks are added. |
| 884 | bool AddedSafetyChecks = false; |
| 885 | |
| 886 | // Holds the end values for each induction variable. We save the end values |
| 887 | // so we can later fix-up the external users of the induction variables. |
| 888 | DenseMap<PHINode *, Value *> IVEndValues; |
| 889 | |
| 890 | // Vector of original scalar PHIs whose corresponding widened PHIs need to be |
| 891 | // fixed up at the end of vector code generation. |
| 892 | SmallVector<PHINode *, 8> OrigPHIsToFix; |
| 893 | |
| 894 | /// BFI and PSI are used to check for profile guided size optimizations. |
| 895 | BlockFrequencyInfo *BFI; |
| 896 | ProfileSummaryInfo *PSI; |
| 897 | |
| 898 | // Whether this loop should be optimized for size based on profile guided size |
| 899 | // optimizatios. |
| 900 | bool OptForSizeBasedOnProfile; |
| 901 | }; |
| 902 | |
| 903 | class InnerLoopUnroller : public InnerLoopVectorizer { |
| 904 | public: |
| 905 | InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| 906 | LoopInfo *LI, DominatorTree *DT, |
| 907 | const TargetLibraryInfo *TLI, |
| 908 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 909 | OptimizationRemarkEmitter *ORE, unsigned UnrollFactor, |
| 910 | LoopVectorizationLegality *LVL, |
| 911 | LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, |
| 912 | ProfileSummaryInfo *PSI) |
| 913 | : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 914 | ElementCount::getFixed(1), UnrollFactor, LVL, CM, |
| 915 | BFI, PSI) {} |
| 916 | |
| 917 | private: |
| 918 | Value *getBroadcastInstrs(Value *V) override; |
| 919 | Value *getStepVector(Value *Val, int StartIdx, Value *Step, |
| 920 | Instruction::BinaryOps Opcode = |
| 921 | Instruction::BinaryOpsEnd) override; |
| 922 | Value *reverseVector(Value *Vec) override; |
| 923 | }; |
| 924 | |
| 925 | /// Encapsulate information regarding vectorization of a loop and its epilogue. |
| 926 | /// This information is meant to be updated and used across two stages of |
| 927 | /// epilogue vectorization. |
| 928 | struct EpilogueLoopVectorizationInfo { |
| 929 | ElementCount MainLoopVF = ElementCount::getFixed(0); |
| 930 | unsigned MainLoopUF = 0; |
| 931 | ElementCount EpilogueVF = ElementCount::getFixed(0); |
| 932 | unsigned EpilogueUF = 0; |
| 933 | BasicBlock *MainLoopIterationCountCheck = nullptr; |
| 934 | BasicBlock *EpilogueIterationCountCheck = nullptr; |
| 935 | BasicBlock *SCEVSafetyCheck = nullptr; |
| 936 | BasicBlock *MemSafetyCheck = nullptr; |
| 937 | Value *TripCount = nullptr; |
| 938 | Value *VectorTripCount = nullptr; |
| 939 | |
| 940 | EpilogueLoopVectorizationInfo(unsigned MVF, unsigned MUF, unsigned EVF, |
| 941 | unsigned EUF) |
| 942 | : MainLoopVF(ElementCount::getFixed(MVF)), MainLoopUF(MUF), |
| 943 | EpilogueVF(ElementCount::getFixed(EVF)), EpilogueUF(EUF) { |
| 944 | assert(EUF == 1 && |
| 945 | "A high UF for the epilogue loop is likely not beneficial." ); |
| 946 | } |
| 947 | }; |
| 948 | |
| 949 | /// An extension of the inner loop vectorizer that creates a skeleton for a |
| 950 | /// vectorized loop that has its epilogue (residual) also vectorized. |
| 951 | /// The idea is to run the vplan on a given loop twice, firstly to setup the |
| 952 | /// skeleton and vectorize the main loop, and secondly to complete the skeleton |
| 953 | /// from the first step and vectorize the epilogue. This is achieved by |
| 954 | /// deriving two concrete strategy classes from this base class and invoking |
| 955 | /// them in succession from the loop vectorizer planner. |
| 956 | class InnerLoopAndEpilogueVectorizer : public InnerLoopVectorizer { |
| 957 | public: |
| 958 | InnerLoopAndEpilogueVectorizer( |
| 959 | Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 960 | DominatorTree *DT, const TargetLibraryInfo *TLI, |
| 961 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 962 | OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI, |
| 963 | LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM, |
| 964 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI) |
| 965 | : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 966 | EPI.MainLoopVF, EPI.MainLoopUF, LVL, CM, BFI, PSI), |
| 967 | EPI(EPI) {} |
| 968 | |
| 969 | // Override this function to handle the more complex control flow around the |
| 970 | // three loops. |
| 971 | BasicBlock *createVectorizedLoopSkeleton() final override { |
| 972 | return createEpilogueVectorizedLoopSkeleton(); |
| 973 | } |
| 974 | |
| 975 | /// The interface for creating a vectorized skeleton using one of two |
| 976 | /// different strategies, each corresponding to one execution of the vplan |
| 977 | /// as described above. |
| 978 | virtual BasicBlock *createEpilogueVectorizedLoopSkeleton() = 0; |
| 979 | |
| 980 | /// Holds and updates state information required to vectorize the main loop |
| 981 | /// and its epilogue in two separate passes. This setup helps us avoid |
| 982 | /// regenerating and recomputing runtime safety checks. It also helps us to |
| 983 | /// shorten the iteration-count-check path length for the cases where the |
| 984 | /// iteration count of the loop is so small that the main vector loop is |
| 985 | /// completely skipped. |
| 986 | EpilogueLoopVectorizationInfo &EPI; |
| 987 | }; |
| 988 | |
| 989 | /// A specialized derived class of inner loop vectorizer that performs |
| 990 | /// vectorization of *main* loops in the process of vectorizing loops and their |
| 991 | /// epilogues. |
| 992 | class EpilogueVectorizerMainLoop : public InnerLoopAndEpilogueVectorizer { |
| 993 | public: |
| 994 | EpilogueVectorizerMainLoop( |
| 995 | Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 996 | DominatorTree *DT, const TargetLibraryInfo *TLI, |
| 997 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 998 | OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI, |
| 999 | LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM, |
| 1000 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI) |
| 1001 | : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 1002 | EPI, LVL, CM, BFI, PSI) {} |
| 1003 | /// Implements the interface for creating a vectorized skeleton using the |
| 1004 | /// *main loop* strategy (ie the first pass of vplan execution). |
| 1005 | BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| 1006 | |
| 1007 | protected: |
| 1008 | /// Emits an iteration count bypass check once for the main loop (when \p |
| 1009 | /// ForEpilogue is false) and once for the epilogue loop (when \p |
| 1010 | /// ForEpilogue is true). |
| 1011 | BasicBlock *emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass, |
| 1012 | bool ForEpilogue); |
| 1013 | void printDebugTracesAtStart() override; |
| 1014 | void printDebugTracesAtEnd() override; |
| 1015 | }; |
| 1016 | |
| 1017 | // A specialized derived class of inner loop vectorizer that performs |
| 1018 | // vectorization of *epilogue* loops in the process of vectorizing loops and |
| 1019 | // their epilogues. |
| 1020 | class EpilogueVectorizerEpilogueLoop : public InnerLoopAndEpilogueVectorizer { |
| 1021 | public: |
| 1022 | EpilogueVectorizerEpilogueLoop(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| 1023 | LoopInfo *LI, DominatorTree *DT, |
| 1024 | const TargetLibraryInfo *TLI, |
| 1025 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 1026 | OptimizationRemarkEmitter *ORE, |
| 1027 | EpilogueLoopVectorizationInfo &EPI, |
| 1028 | LoopVectorizationLegality *LVL, |
| 1029 | llvm::LoopVectorizationCostModel *CM, |
| 1030 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI) |
| 1031 | : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 1032 | EPI, LVL, CM, BFI, PSI) {} |
| 1033 | /// Implements the interface for creating a vectorized skeleton using the |
| 1034 | /// *epilogue loop* strategy (ie the second pass of vplan execution). |
| 1035 | BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| 1036 | |
| 1037 | protected: |
| 1038 | /// Emits an iteration count bypass check after the main vector loop has |
| 1039 | /// finished to see if there are any iterations left to execute by either |
| 1040 | /// the vector epilogue or the scalar epilogue. |
| 1041 | BasicBlock *emitMinimumVectorEpilogueIterCountCheck(Loop *L, |
| 1042 | BasicBlock *Bypass, |
| 1043 | BasicBlock *Insert); |
| 1044 | void printDebugTracesAtStart() override; |
| 1045 | void printDebugTracesAtEnd() override; |
| 1046 | }; |
| 1047 | } // end namespace llvm |
| 1048 | |
| 1049 | /// Look for a meaningful debug location on the instruction or it's |
| 1050 | /// operands. |
| 1051 | static Instruction *getDebugLocFromInstOrOperands(Instruction *I) { |
| 1052 | if (!I) |
| 1053 | return I; |
| 1054 | |
| 1055 | DebugLoc Empty; |
| 1056 | if (I->getDebugLoc() != Empty) |
| 1057 | return I; |
| 1058 | |
| 1059 | for (User::op_iterator OI = I->op_begin(), OE = I->op_end(); OI != OE; ++OI) { |
| 1060 | if (Instruction *OpInst = dyn_cast<Instruction>(*OI)) |
| 1061 | if (OpInst->getDebugLoc() != Empty) |
| 1062 | return OpInst; |
| 1063 | } |
| 1064 | |
| 1065 | return I; |
| 1066 | } |
| 1067 | |
| 1068 | void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) { |
| 1069 | if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr)) { |
| 1070 | const DILocation *DIL = Inst->getDebugLoc(); |
| 1071 | if (DIL && Inst->getFunction()->isDebugInfoForProfiling() && |
| 1072 | !isa<DbgInfoIntrinsic>(Inst)) { |
| 1073 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 1074 | auto NewDIL = |
| 1075 | DIL->cloneByMultiplyingDuplicationFactor(UF * VF.getKnownMinValue()); |
| 1076 | if (NewDIL) |
| 1077 | B.SetCurrentDebugLocation(NewDIL.getValue()); |
| 1078 | else |
| 1079 | LLVM_DEBUG(dbgs() |
| 1080 | << "Failed to create new discriminator: " |
| 1081 | << DIL->getFilename() << " Line: " << DIL->getLine()); |
| 1082 | } |
| 1083 | else |
| 1084 | B.SetCurrentDebugLocation(DIL); |
| 1085 | } else |
| 1086 | B.SetCurrentDebugLocation(DebugLoc()); |
| 1087 | } |
| 1088 | |
| 1089 | /// Write a record \p DebugMsg about vectorization failure to the debug |
| 1090 | /// output stream. If \p I is passed, it is an instruction that prevents |
| 1091 | /// vectorization. |
| 1092 | #ifndef NDEBUG |
| 1093 | static void debugVectorizationFailure(const StringRef DebugMsg, |
| 1094 | Instruction *I) { |
| 1095 | dbgs() << "LV: Not vectorizing: " << DebugMsg; |
| 1096 | if (I != nullptr) |
| 1097 | dbgs() << " " << *I; |
| 1098 | else |
| 1099 | dbgs() << '.'; |
| 1100 | dbgs() << '\n'; |
| 1101 | } |
| 1102 | #endif |
| 1103 | |
| 1104 | /// Create an analysis remark that explains why vectorization failed |
| 1105 | /// |
| 1106 | /// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p |
| 1107 | /// RemarkName is the identifier for the remark. If \p I is passed it is an |
| 1108 | /// instruction that prevents vectorization. Otherwise \p TheLoop is used for |
| 1109 | /// the location of the remark. \return the remark object that can be |
| 1110 | /// streamed to. |
| 1111 | static OptimizationRemarkAnalysis createLVAnalysis(const char *PassName, |
| 1112 | StringRef , Loop *TheLoop, Instruction *I) { |
| 1113 | Value *CodeRegion = TheLoop->getHeader(); |
| 1114 | DebugLoc DL = TheLoop->getStartLoc(); |
| 1115 | |
| 1116 | if (I) { |
| 1117 | CodeRegion = I->getParent(); |
| 1118 | // If there is no debug location attached to the instruction, revert back to |
| 1119 | // using the loop's. |
| 1120 | if (I->getDebugLoc()) |
| 1121 | DL = I->getDebugLoc(); |
| 1122 | } |
| 1123 | |
| 1124 | OptimizationRemarkAnalysis R(PassName, RemarkName, DL, CodeRegion); |
| 1125 | R << "loop not vectorized: " ; |
| 1126 | return R; |
| 1127 | } |
| 1128 | |
| 1129 | /// Return a value for Step multiplied by VF. |
| 1130 | static Value *createStepForVF(IRBuilder<> &B, Constant *Step, ElementCount VF) { |
| 1131 | assert(isa<ConstantInt>(Step) && "Expected an integer step" ); |
| 1132 | Constant *StepVal = ConstantInt::get( |
| 1133 | Step->getType(), |
| 1134 | cast<ConstantInt>(Step)->getSExtValue() * VF.getKnownMinValue()); |
| 1135 | return VF.isScalable() ? B.CreateVScale(StepVal) : StepVal; |
| 1136 | } |
| 1137 | |
| 1138 | namespace llvm { |
| 1139 | |
| 1140 | void (const StringRef DebugMsg, |
| 1141 | const StringRef OREMsg, const StringRef ORETag, |
| 1142 | OptimizationRemarkEmitter *ORE, Loop *TheLoop, Instruction *I) { |
| 1143 | LLVM_DEBUG(debugVectorizationFailure(DebugMsg, I)); |
| 1144 | LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE); |
| 1145 | ORE->emit(createLVAnalysis(Hints.vectorizeAnalysisPassName(), |
| 1146 | ORETag, TheLoop, I) << OREMsg); |
| 1147 | } |
| 1148 | |
| 1149 | } // end namespace llvm |
| 1150 | |
| 1151 | #ifndef NDEBUG |
| 1152 | /// \return string containing a file name and a line # for the given loop. |
| 1153 | static std::string getDebugLocString(const Loop *L) { |
| 1154 | std::string Result; |
| 1155 | if (L) { |
| 1156 | raw_string_ostream OS(Result); |
| 1157 | if (const DebugLoc LoopDbgLoc = L->getStartLoc()) |
| 1158 | LoopDbgLoc.print(OS); |
| 1159 | else |
| 1160 | // Just print the module name. |
| 1161 | OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier(); |
| 1162 | OS.flush(); |
| 1163 | } |
| 1164 | return Result; |
| 1165 | } |
| 1166 | #endif |
| 1167 | |
| 1168 | void InnerLoopVectorizer::addNewMetadata(Instruction *To, |
| 1169 | const Instruction *Orig) { |
| 1170 | // If the loop was versioned with memchecks, add the corresponding no-alias |
| 1171 | // metadata. |
| 1172 | if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig))) |
| 1173 | LVer->annotateInstWithNoAlias(To, Orig); |
| 1174 | } |
| 1175 | |
| 1176 | void InnerLoopVectorizer::addMetadata(Instruction *To, |
| 1177 | Instruction *From) { |
| 1178 | propagateMetadata(To, From); |
| 1179 | addNewMetadata(To, From); |
| 1180 | } |
| 1181 | |
| 1182 | void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To, |
| 1183 | Instruction *From) { |
| 1184 | for (Value *V : To) { |
| 1185 | if (Instruction *I = dyn_cast<Instruction>(V)) |
| 1186 | addMetadata(I, From); |
| 1187 | } |
| 1188 | } |
| 1189 | |
| 1190 | namespace llvm { |
| 1191 | |
| 1192 | // Loop vectorization cost-model hints how the scalar epilogue loop should be |
| 1193 | // lowered. |
| 1194 | enum ScalarEpilogueLowering { |
| 1195 | |
| 1196 | // The default: allowing scalar epilogues. |
| 1197 | CM_ScalarEpilogueAllowed, |
| 1198 | |
| 1199 | // Vectorization with OptForSize: don't allow epilogues. |
| 1200 | CM_ScalarEpilogueNotAllowedOptSize, |
| 1201 | |
| 1202 | // A special case of vectorisation with OptForSize: loops with a very small |
| 1203 | // trip count are considered for vectorization under OptForSize, thereby |
| 1204 | // making sure the cost of their loop body is dominant, free of runtime |
| 1205 | // guards and scalar iteration overheads. |
| 1206 | CM_ScalarEpilogueNotAllowedLowTripLoop, |
| 1207 | |
| 1208 | // Loop hint predicate indicating an epilogue is undesired. |
| 1209 | CM_ScalarEpilogueNotNeededUsePredicate, |
| 1210 | |
| 1211 | // Directive indicating we must either tail fold or not vectorize |
| 1212 | CM_ScalarEpilogueNotAllowedUsePredicate |
| 1213 | }; |
| 1214 | |
| 1215 | /// LoopVectorizationCostModel - estimates the expected speedups due to |
| 1216 | /// vectorization. |
| 1217 | /// In many cases vectorization is not profitable. This can happen because of |
| 1218 | /// a number of reasons. In this class we mainly attempt to predict the |
| 1219 | /// expected speedup/slowdowns due to the supported instruction set. We use the |
| 1220 | /// TargetTransformInfo to query the different backends for the cost of |
| 1221 | /// different operations. |
| 1222 | class LoopVectorizationCostModel { |
| 1223 | public: |
| 1224 | LoopVectorizationCostModel(ScalarEpilogueLowering SEL, Loop *L, |
| 1225 | PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 1226 | LoopVectorizationLegality *Legal, |
| 1227 | const TargetTransformInfo &TTI, |
| 1228 | const TargetLibraryInfo *TLI, DemandedBits *DB, |
| 1229 | AssumptionCache *AC, |
| 1230 | OptimizationRemarkEmitter *ORE, const Function *F, |
| 1231 | const LoopVectorizeHints *Hints, |
| 1232 | InterleavedAccessInfo &IAI) |
| 1233 | : ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal), |
| 1234 | TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F), |
| 1235 | Hints(Hints), InterleaveInfo(IAI) {} |
| 1236 | |
| 1237 | /// \return An upper bound for the vectorization factor, or None if |
| 1238 | /// vectorization and interleaving should be avoided up front. |
| 1239 | Optional<ElementCount> computeMaxVF(ElementCount UserVF, unsigned UserIC); |
| 1240 | |
| 1241 | /// \return True if runtime checks are required for vectorization, and false |
| 1242 | /// otherwise. |
| 1243 | bool runtimeChecksRequired(); |
| 1244 | |
| 1245 | /// \return The most profitable vectorization factor and the cost of that VF. |
| 1246 | /// This method checks every power of two up to MaxVF. If UserVF is not ZERO |
| 1247 | /// then this vectorization factor will be selected if vectorization is |
| 1248 | /// possible. |
| 1249 | VectorizationFactor selectVectorizationFactor(ElementCount MaxVF); |
| 1250 | VectorizationFactor |
| 1251 | selectEpilogueVectorizationFactor(const ElementCount MaxVF, |
| 1252 | const LoopVectorizationPlanner &LVP); |
| 1253 | |
| 1254 | /// Setup cost-based decisions for user vectorization factor. |
| 1255 | void selectUserVectorizationFactor(ElementCount UserVF) { |
| 1256 | collectUniformsAndScalars(UserVF); |
| 1257 | collectInstsToScalarize(UserVF); |
| 1258 | } |
| 1259 | |
| 1260 | /// \return The size (in bits) of the smallest and widest types in the code |
| 1261 | /// that needs to be vectorized. We ignore values that remain scalar such as |
| 1262 | /// 64 bit loop indices. |
| 1263 | std::pair<unsigned, unsigned> getSmallestAndWidestTypes(); |
| 1264 | |
| 1265 | /// \return The desired interleave count. |
| 1266 | /// If interleave count has been specified by metadata it will be returned. |
| 1267 | /// Otherwise, the interleave count is computed and returned. VF and LoopCost |
| 1268 | /// are the selected vectorization factor and the cost of the selected VF. |
| 1269 | unsigned selectInterleaveCount(ElementCount VF, unsigned LoopCost); |
| 1270 | |
| 1271 | /// Memory access instruction may be vectorized in more than one way. |
| 1272 | /// Form of instruction after vectorization depends on cost. |
| 1273 | /// This function takes cost-based decisions for Load/Store instructions |
| 1274 | /// and collects them in a map. This decisions map is used for building |
| 1275 | /// the lists of loop-uniform and loop-scalar instructions. |
| 1276 | /// The calculated cost is saved with widening decision in order to |
| 1277 | /// avoid redundant calculations. |
| 1278 | void setCostBasedWideningDecision(ElementCount VF); |
| 1279 | |
| 1280 | /// A struct that represents some properties of the register usage |
| 1281 | /// of a loop. |
| 1282 | struct RegisterUsage { |
| 1283 | /// Holds the number of loop invariant values that are used in the loop. |
| 1284 | /// The key is ClassID of target-provided register class. |
| 1285 | SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs; |
| 1286 | /// Holds the maximum number of concurrent live intervals in the loop. |
| 1287 | /// The key is ClassID of target-provided register class. |
| 1288 | SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers; |
| 1289 | }; |
| 1290 | |
| 1291 | /// \return Returns information about the register usages of the loop for the |
| 1292 | /// given vectorization factors. |
| 1293 | SmallVector<RegisterUsage, 8> |
| 1294 | calculateRegisterUsage(ArrayRef<ElementCount> VFs); |
| 1295 | |
| 1296 | /// Collect values we want to ignore in the cost model. |
| 1297 | void collectValuesToIgnore(); |
| 1298 | |
| 1299 | /// Split reductions into those that happen in the loop, and those that happen |
| 1300 | /// outside. In loop reductions are collected into InLoopReductionChains. |
| 1301 | void collectInLoopReductions(); |
| 1302 | |
| 1303 | /// \returns The smallest bitwidth each instruction can be represented with. |
| 1304 | /// The vector equivalents of these instructions should be truncated to this |
| 1305 | /// type. |
| 1306 | const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const { |
| 1307 | return MinBWs; |
| 1308 | } |
| 1309 | |
| 1310 | /// \returns True if it is more profitable to scalarize instruction \p I for |
| 1311 | /// vectorization factor \p VF. |
| 1312 | bool isProfitableToScalarize(Instruction *I, ElementCount VF) const { |
| 1313 | assert(VF.isVector() && |
| 1314 | "Profitable to scalarize relevant only for VF > 1." ); |
| 1315 | |
| 1316 | // Cost model is not run in the VPlan-native path - return conservative |
| 1317 | // result until this changes. |
| 1318 | if (EnableVPlanNativePath) |
| 1319 | return false; |
| 1320 | |
| 1321 | auto Scalars = InstsToScalarize.find(VF); |
| 1322 | assert(Scalars != InstsToScalarize.end() && |
| 1323 | "VF not yet analyzed for scalarization profitability" ); |
| 1324 | return Scalars->second.find(I) != Scalars->second.end(); |
| 1325 | } |
| 1326 | |
| 1327 | /// Returns true if \p I is known to be uniform after vectorization. |
| 1328 | bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const { |
| 1329 | if (VF.isScalar()) |
| 1330 | return true; |
| 1331 | |
| 1332 | // Cost model is not run in the VPlan-native path - return conservative |
| 1333 | // result until this changes. |
| 1334 | if (EnableVPlanNativePath) |
| 1335 | return false; |
| 1336 | |
| 1337 | auto UniformsPerVF = Uniforms.find(VF); |
| 1338 | assert(UniformsPerVF != Uniforms.end() && |
| 1339 | "VF not yet analyzed for uniformity" ); |
| 1340 | return UniformsPerVF->second.count(I); |
| 1341 | } |
| 1342 | |
| 1343 | /// Returns true if \p I is known to be scalar after vectorization. |
| 1344 | bool isScalarAfterVectorization(Instruction *I, ElementCount VF) const { |
| 1345 | if (VF.isScalar()) |
| 1346 | return true; |
| 1347 | |
| 1348 | // Cost model is not run in the VPlan-native path - return conservative |
| 1349 | // result until this changes. |
| 1350 | if (EnableVPlanNativePath) |
| 1351 | return false; |
| 1352 | |
| 1353 | auto ScalarsPerVF = Scalars.find(VF); |
| 1354 | assert(ScalarsPerVF != Scalars.end() && |
| 1355 | "Scalar values are not calculated for VF" ); |
| 1356 | return ScalarsPerVF->second.count(I); |
| 1357 | } |
| 1358 | |
| 1359 | /// \returns True if instruction \p I can be truncated to a smaller bitwidth |
| 1360 | /// for vectorization factor \p VF. |
| 1361 | bool canTruncateToMinimalBitwidth(Instruction *I, ElementCount VF) const { |
| 1362 | return VF.isVector() && MinBWs.find(I) != MinBWs.end() && |
| 1363 | !isProfitableToScalarize(I, VF) && |
| 1364 | !isScalarAfterVectorization(I, VF); |
| 1365 | } |
| 1366 | |
| 1367 | /// Decision that was taken during cost calculation for memory instruction. |
| 1368 | enum InstWidening { |
| 1369 | CM_Unknown, |
| 1370 | CM_Widen, // For consecutive accesses with stride +1. |
| 1371 | CM_Widen_Reverse, // For consecutive accesses with stride -1. |
| 1372 | CM_Interleave, |
| 1373 | CM_GatherScatter, |
| 1374 | CM_Scalarize |
| 1375 | }; |
| 1376 | |
| 1377 | /// Save vectorization decision \p W and \p Cost taken by the cost model for |
| 1378 | /// instruction \p I and vector width \p VF. |
| 1379 | void setWideningDecision(Instruction *I, ElementCount VF, InstWidening W, |
| 1380 | InstructionCost Cost) { |
| 1381 | assert(VF.isVector() && "Expected VF >=2" ); |
| 1382 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost); |
| 1383 | } |
| 1384 | |
| 1385 | /// Save vectorization decision \p W and \p Cost taken by the cost model for |
| 1386 | /// interleaving group \p Grp and vector width \p VF. |
| 1387 | void setWideningDecision(const InterleaveGroup<Instruction> *Grp, |
| 1388 | ElementCount VF, InstWidening W, |
| 1389 | InstructionCost Cost) { |
| 1390 | assert(VF.isVector() && "Expected VF >=2" ); |
| 1391 | /// Broadcast this decicion to all instructions inside the group. |
| 1392 | /// But the cost will be assigned to one instruction only. |
| 1393 | for (unsigned i = 0; i < Grp->getFactor(); ++i) { |
| 1394 | if (auto *I = Grp->getMember(i)) { |
| 1395 | if (Grp->getInsertPos() == I) |
| 1396 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost); |
| 1397 | else |
| 1398 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0); |
| 1399 | } |
| 1400 | } |
| 1401 | } |
| 1402 | |
| 1403 | /// Return the cost model decision for the given instruction \p I and vector |
| 1404 | /// width \p VF. Return CM_Unknown if this instruction did not pass |
| 1405 | /// through the cost modeling. |
| 1406 | InstWidening getWideningDecision(Instruction *I, ElementCount VF) { |
| 1407 | assert(VF.isVector() && "Expected VF to be a vector VF" ); |
| 1408 | // Cost model is not run in the VPlan-native path - return conservative |
| 1409 | // result until this changes. |
| 1410 | if (EnableVPlanNativePath) |
| 1411 | return CM_GatherScatter; |
| 1412 | |
| 1413 | std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| 1414 | auto Itr = WideningDecisions.find(InstOnVF); |
| 1415 | if (Itr == WideningDecisions.end()) |
| 1416 | return CM_Unknown; |
| 1417 | return Itr->second.first; |
| 1418 | } |
| 1419 | |
| 1420 | /// Return the vectorization cost for the given instruction \p I and vector |
| 1421 | /// width \p VF. |
| 1422 | InstructionCost getWideningCost(Instruction *I, ElementCount VF) { |
| 1423 | assert(VF.isVector() && "Expected VF >=2" ); |
| 1424 | std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| 1425 | assert(WideningDecisions.find(InstOnVF) != WideningDecisions.end() && |
| 1426 | "The cost is not calculated" ); |
| 1427 | return WideningDecisions[InstOnVF].second; |
| 1428 | } |
| 1429 | |
| 1430 | /// Return True if instruction \p I is an optimizable truncate whose operand |
| 1431 | /// is an induction variable. Such a truncate will be removed by adding a new |
| 1432 | /// induction variable with the destination type. |
| 1433 | bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) { |
| 1434 | // If the instruction is not a truncate, return false. |
| 1435 | auto *Trunc = dyn_cast<TruncInst>(I); |
| 1436 | if (!Trunc) |
| 1437 | return false; |
| 1438 | |
| 1439 | // Get the source and destination types of the truncate. |
| 1440 | Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF); |
| 1441 | Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF); |
| 1442 | |
| 1443 | // If the truncate is free for the given types, return false. Replacing a |
| 1444 | // free truncate with an induction variable would add an induction variable |
| 1445 | // update instruction to each iteration of the loop. We exclude from this |
| 1446 | // check the primary induction variable since it will need an update |
| 1447 | // instruction regardless. |
| 1448 | Value *Op = Trunc->getOperand(0); |
| 1449 | if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy)) |
| 1450 | return false; |
| 1451 | |
| 1452 | // If the truncated value is not an induction variable, return false. |
| 1453 | return Legal->isInductionPhi(Op); |
| 1454 | } |
| 1455 | |
| 1456 | /// Collects the instructions to scalarize for each predicated instruction in |
| 1457 | /// the loop. |
| 1458 | void collectInstsToScalarize(ElementCount VF); |
| 1459 | |
| 1460 | /// Collect Uniform and Scalar values for the given \p VF. |
| 1461 | /// The sets depend on CM decision for Load/Store instructions |
| 1462 | /// that may be vectorized as interleave, gather-scatter or scalarized. |
| 1463 | void collectUniformsAndScalars(ElementCount VF) { |
| 1464 | // Do the analysis once. |
| 1465 | if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end()) |
| 1466 | return; |
| 1467 | setCostBasedWideningDecision(VF); |
| 1468 | collectLoopUniforms(VF); |
| 1469 | collectLoopScalars(VF); |
| 1470 | } |
| 1471 | |
| 1472 | /// Returns true if the target machine supports masked store operation |
| 1473 | /// for the given \p DataType and kind of access to \p Ptr. |
| 1474 | bool isLegalMaskedStore(Type *DataType, Value *Ptr, Align Alignment) { |
| 1475 | return Legal->isConsecutivePtr(Ptr) && |
| 1476 | TTI.isLegalMaskedStore(DataType, Alignment); |
| 1477 | } |
| 1478 | |
| 1479 | /// Returns true if the target machine supports masked load operation |
| 1480 | /// for the given \p DataType and kind of access to \p Ptr. |
| 1481 | bool isLegalMaskedLoad(Type *DataType, Value *Ptr, Align Alignment) { |
| 1482 | return Legal->isConsecutivePtr(Ptr) && |
| 1483 | TTI.isLegalMaskedLoad(DataType, Alignment); |
| 1484 | } |
| 1485 | |
| 1486 | /// Returns true if the target machine supports masked scatter operation |
| 1487 | /// for the given \p DataType. |
| 1488 | bool isLegalMaskedScatter(Type *DataType, Align Alignment) { |
| 1489 | return TTI.isLegalMaskedScatter(DataType, Alignment); |
| 1490 | } |
| 1491 | |
| 1492 | /// Returns true if the target machine supports masked gather operation |
| 1493 | /// for the given \p DataType. |
| 1494 | bool isLegalMaskedGather(Type *DataType, Align Alignment) { |
| 1495 | return TTI.isLegalMaskedGather(DataType, Alignment); |
| 1496 | } |
| 1497 | |
| 1498 | /// Returns true if the target machine can represent \p V as a masked gather |
| 1499 | /// or scatter operation. |
| 1500 | bool isLegalGatherOrScatter(Value *V) { |
| 1501 | bool LI = isa<LoadInst>(V); |
| 1502 | bool SI = isa<StoreInst>(V); |
| 1503 | if (!LI && !SI) |
| 1504 | return false; |
| 1505 | auto *Ty = getMemInstValueType(V); |
| 1506 | Align Align = getLoadStoreAlignment(V); |
| 1507 | return (LI && isLegalMaskedGather(Ty, Align)) || |
| 1508 | (SI && isLegalMaskedScatter(Ty, Align)); |
| 1509 | } |
| 1510 | |
| 1511 | /// Returns true if \p I is an instruction that will be scalarized with |
| 1512 | /// predication. Such instructions include conditional stores and |
| 1513 | /// instructions that may divide by zero. |
| 1514 | /// If a non-zero VF has been calculated, we check if I will be scalarized |
| 1515 | /// predication for that VF. |
| 1516 | bool isScalarWithPredication(Instruction *I, |
| 1517 | ElementCount VF = ElementCount::getFixed(1)); |
| 1518 | |
| 1519 | // Returns true if \p I is an instruction that will be predicated either |
| 1520 | // through scalar predication or masked load/store or masked gather/scatter. |
| 1521 | // Superset of instructions that return true for isScalarWithPredication. |
| 1522 | bool isPredicatedInst(Instruction *I) { |
| 1523 | if (!blockNeedsPredication(I->getParent())) |
| 1524 | return false; |
| 1525 | // Loads and stores that need some form of masked operation are predicated |
| 1526 | // instructions. |
| 1527 | if (isa<LoadInst>(I) || isa<StoreInst>(I)) |
| 1528 | return Legal->isMaskRequired(I); |
| 1529 | return isScalarWithPredication(I); |
| 1530 | } |
| 1531 | |
| 1532 | /// Returns true if \p I is a memory instruction with consecutive memory |
| 1533 | /// access that can be widened. |
| 1534 | bool |
| 1535 | memoryInstructionCanBeWidened(Instruction *I, |
| 1536 | ElementCount VF = ElementCount::getFixed(1)); |
| 1537 | |
| 1538 | /// Returns true if \p I is a memory instruction in an interleaved-group |
| 1539 | /// of memory accesses that can be vectorized with wide vector loads/stores |
| 1540 | /// and shuffles. |
| 1541 | bool |
| 1542 | interleavedAccessCanBeWidened(Instruction *I, |
| 1543 | ElementCount VF = ElementCount::getFixed(1)); |
| 1544 | |
| 1545 | /// Check if \p Instr belongs to any interleaved access group. |
| 1546 | bool isAccessInterleaved(Instruction *Instr) { |
| 1547 | return InterleaveInfo.isInterleaved(Instr); |
| 1548 | } |
| 1549 | |
| 1550 | /// Get the interleaved access group that \p Instr belongs to. |
| 1551 | const InterleaveGroup<Instruction> * |
| 1552 | getInterleavedAccessGroup(Instruction *Instr) { |
| 1553 | return InterleaveInfo.getInterleaveGroup(Instr); |
| 1554 | } |
| 1555 | |
| 1556 | /// Returns true if we're required to use a scalar epilogue for at least |
| 1557 | /// the final iteration of the original loop. |
| 1558 | bool requiresScalarEpilogue() const { |
| 1559 | if (!isScalarEpilogueAllowed()) |
| 1560 | return false; |
| 1561 | // If we might exit from anywhere but the latch, must run the exiting |
| 1562 | // iteration in scalar form. |
| 1563 | if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) |
| 1564 | return true; |
| 1565 | return InterleaveInfo.requiresScalarEpilogue(); |
| 1566 | } |
| 1567 | |
| 1568 | /// Returns true if a scalar epilogue is not allowed due to optsize or a |
| 1569 | /// loop hint annotation. |
| 1570 | bool isScalarEpilogueAllowed() const { |
| 1571 | return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed; |
| 1572 | } |
| 1573 | |
| 1574 | /// Returns true if all loop blocks should be masked to fold tail loop. |
| 1575 | bool foldTailByMasking() const { return FoldTailByMasking; } |
| 1576 | |
| 1577 | bool blockNeedsPredication(BasicBlock *BB) { |
| 1578 | return foldTailByMasking() || Legal->blockNeedsPredication(BB); |
| 1579 | } |
| 1580 | |
| 1581 | /// A SmallMapVector to store the InLoop reduction op chains, mapping phi |
| 1582 | /// nodes to the chain of instructions representing the reductions. Uses a |
| 1583 | /// MapVector to ensure deterministic iteration order. |
| 1584 | using ReductionChainMap = |
| 1585 | SmallMapVector<PHINode *, SmallVector<Instruction *, 4>, 4>; |
| 1586 | |
| 1587 | /// Return the chain of instructions representing an inloop reduction. |
| 1588 | const ReductionChainMap &getInLoopReductionChains() const { |
| 1589 | return InLoopReductionChains; |
| 1590 | } |
| 1591 | |
| 1592 | /// Returns true if the Phi is part of an inloop reduction. |
| 1593 | bool isInLoopReduction(PHINode *Phi) const { |
| 1594 | return InLoopReductionChains.count(Phi); |
| 1595 | } |
| 1596 | |
| 1597 | /// Estimate cost of an intrinsic call instruction CI if it were vectorized |
| 1598 | /// with factor VF. Return the cost of the instruction, including |
| 1599 | /// scalarization overhead if it's needed. |
| 1600 | InstructionCost getVectorIntrinsicCost(CallInst *CI, ElementCount VF); |
| 1601 | |
| 1602 | /// Estimate cost of a call instruction CI if it were vectorized with factor |
| 1603 | /// VF. Return the cost of the instruction, including scalarization overhead |
| 1604 | /// if it's needed. The flag NeedToScalarize shows if the call needs to be |
| 1605 | /// scalarized - |
| 1606 | /// i.e. either vector version isn't available, or is too expensive. |
| 1607 | InstructionCost getVectorCallCost(CallInst *CI, ElementCount VF, |
| 1608 | bool &NeedToScalarize); |
| 1609 | |
| 1610 | /// Invalidates decisions already taken by the cost model. |
| 1611 | void invalidateCostModelingDecisions() { |
| 1612 | WideningDecisions.clear(); |
| 1613 | Uniforms.clear(); |
| 1614 | Scalars.clear(); |
| 1615 | } |
| 1616 | |
| 1617 | private: |
| 1618 | unsigned NumPredStores = 0; |
| 1619 | |
| 1620 | /// \return An upper bound for the vectorization factor, a power-of-2 larger |
| 1621 | /// than zero. One is returned if vectorization should best be avoided due |
| 1622 | /// to cost. |
| 1623 | ElementCount computeFeasibleMaxVF(unsigned ConstTripCount, |
| 1624 | ElementCount UserVF); |
| 1625 | |
| 1626 | /// The vectorization cost is a combination of the cost itself and a boolean |
| 1627 | /// indicating whether any of the contributing operations will actually |
| 1628 | /// operate on |
| 1629 | /// vector values after type legalization in the backend. If this latter value |
| 1630 | /// is |
| 1631 | /// false, then all operations will be scalarized (i.e. no vectorization has |
| 1632 | /// actually taken place). |
| 1633 | using VectorizationCostTy = std::pair<InstructionCost, bool>; |
| 1634 | |
| 1635 | /// Returns the expected execution cost. The unit of the cost does |
| 1636 | /// not matter because we use the 'cost' units to compare different |
| 1637 | /// vector widths. The cost that is returned is *not* normalized by |
| 1638 | /// the factor width. |
| 1639 | VectorizationCostTy expectedCost(ElementCount VF); |
| 1640 | |
| 1641 | /// Returns the execution time cost of an instruction for a given vector |
| 1642 | /// width. Vector width of one means scalar. |
| 1643 | VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF); |
| 1644 | |
| 1645 | /// The cost-computation logic from getInstructionCost which provides |
| 1646 | /// the vector type as an output parameter. |
| 1647 | InstructionCost getInstructionCost(Instruction *I, ElementCount VF, |
| 1648 | Type *&VectorTy); |
| 1649 | |
| 1650 | /// Return the cost of instructions in an inloop reduction pattern, if I is |
| 1651 | /// part of that pattern. |
| 1652 | InstructionCost getReductionPatternCost(Instruction *I, ElementCount VF, |
| 1653 | Type *VectorTy, |
| 1654 | TTI::TargetCostKind CostKind); |
| 1655 | |
| 1656 | /// Calculate vectorization cost of memory instruction \p I. |
| 1657 | InstructionCost getMemoryInstructionCost(Instruction *I, ElementCount VF); |
| 1658 | |
| 1659 | /// The cost computation for scalarized memory instruction. |
| 1660 | InstructionCost getMemInstScalarizationCost(Instruction *I, ElementCount VF); |
| 1661 | |
| 1662 | /// The cost computation for interleaving group of memory instructions. |
| 1663 | InstructionCost getInterleaveGroupCost(Instruction *I, ElementCount VF); |
| 1664 | |
| 1665 | /// The cost computation for Gather/Scatter instruction. |
| 1666 | InstructionCost getGatherScatterCost(Instruction *I, ElementCount VF); |
| 1667 | |
| 1668 | /// The cost computation for widening instruction \p I with consecutive |
| 1669 | /// memory access. |
| 1670 | InstructionCost getConsecutiveMemOpCost(Instruction *I, ElementCount VF); |
| 1671 | |
| 1672 | /// The cost calculation for Load/Store instruction \p I with uniform pointer - |
| 1673 | /// Load: scalar load + broadcast. |
| 1674 | /// Store: scalar store + (loop invariant value stored? 0 : extract of last |
| 1675 | /// element) |
| 1676 | InstructionCost getUniformMemOpCost(Instruction *I, ElementCount VF); |
| 1677 | |
| 1678 | /// Estimate the overhead of scalarizing an instruction. This is a |
| 1679 | /// convenience wrapper for the type-based getScalarizationOverhead API. |
| 1680 | InstructionCost getScalarizationOverhead(Instruction *I, ElementCount VF); |
| 1681 | |
| 1682 | /// Returns whether the instruction is a load or store and will be a emitted |
| 1683 | /// as a vector operation. |
| 1684 | bool isConsecutiveLoadOrStore(Instruction *I); |
| 1685 | |
| 1686 | /// Returns true if an artificially high cost for emulated masked memrefs |
| 1687 | /// should be used. |
| 1688 | bool useEmulatedMaskMemRefHack(Instruction *I); |
| 1689 | |
| 1690 | /// Map of scalar integer values to the smallest bitwidth they can be legally |
| 1691 | /// represented as. The vector equivalents of these values should be truncated |
| 1692 | /// to this type. |
| 1693 | MapVector<Instruction *, uint64_t> MinBWs; |
| 1694 | |
| 1695 | /// A type representing the costs for instructions if they were to be |
| 1696 | /// scalarized rather than vectorized. The entries are Instruction-Cost |
| 1697 | /// pairs. |
| 1698 | using ScalarCostsTy = DenseMap<Instruction *, InstructionCost>; |
| 1699 | |
| 1700 | /// A set containing all BasicBlocks that are known to present after |
| 1701 | /// vectorization as a predicated block. |
| 1702 | SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization; |
| 1703 | |
| 1704 | /// Records whether it is allowed to have the original scalar loop execute at |
| 1705 | /// least once. This may be needed as a fallback loop in case runtime |
| 1706 | /// aliasing/dependence checks fail, or to handle the tail/remainder |
| 1707 | /// iterations when the trip count is unknown or doesn't divide by the VF, |
| 1708 | /// or as a peel-loop to handle gaps in interleave-groups. |
| 1709 | /// Under optsize and when the trip count is very small we don't allow any |
| 1710 | /// iterations to execute in the scalar loop. |
| 1711 | ScalarEpilogueLowering ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 1712 | |
| 1713 | /// All blocks of loop are to be masked to fold tail of scalar iterations. |
| 1714 | bool FoldTailByMasking = false; |
| 1715 | |
| 1716 | /// A map holding scalar costs for different vectorization factors. The |
| 1717 | /// presence of a cost for an instruction in the mapping indicates that the |
| 1718 | /// instruction will be scalarized when vectorizing with the associated |
| 1719 | /// vectorization factor. The entries are VF-ScalarCostTy pairs. |
| 1720 | DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize; |
| 1721 | |
| 1722 | /// Holds the instructions known to be uniform after vectorization. |
| 1723 | /// The data is collected per VF. |
| 1724 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms; |
| 1725 | |
| 1726 | /// Holds the instructions known to be scalar after vectorization. |
| 1727 | /// The data is collected per VF. |
| 1728 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars; |
| 1729 | |
| 1730 | /// Holds the instructions (address computations) that are forced to be |
| 1731 | /// scalarized. |
| 1732 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars; |
| 1733 | |
| 1734 | /// PHINodes of the reductions that should be expanded in-loop along with |
| 1735 | /// their associated chains of reduction operations, in program order from top |
| 1736 | /// (PHI) to bottom |
| 1737 | ReductionChainMap InLoopReductionChains; |
| 1738 | |
| 1739 | /// A Map of inloop reduction operations and their immediate chain operand. |
| 1740 | /// FIXME: This can be removed once reductions can be costed correctly in |
| 1741 | /// vplan. This was added to allow quick lookup to the inloop operations, |
| 1742 | /// without having to loop through InLoopReductionChains. |
| 1743 | DenseMap<Instruction *, Instruction *> InLoopReductionImmediateChains; |
| 1744 | |
| 1745 | /// Returns the expected difference in cost from scalarizing the expression |
| 1746 | /// feeding a predicated instruction \p PredInst. The instructions to |
| 1747 | /// scalarize and their scalar costs are collected in \p ScalarCosts. A |
| 1748 | /// non-negative return value implies the expression will be scalarized. |
| 1749 | /// Currently, only single-use chains are considered for scalarization. |
| 1750 | int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts, |
| 1751 | ElementCount VF); |
| 1752 | |
| 1753 | /// Collect the instructions that are uniform after vectorization. An |
| 1754 | /// instruction is uniform if we represent it with a single scalar value in |
| 1755 | /// the vectorized loop corresponding to each vector iteration. Examples of |
| 1756 | /// uniform instructions include pointer operands of consecutive or |
| 1757 | /// interleaved memory accesses. Note that although uniformity implies an |
| 1758 | /// instruction will be scalar, the reverse is not true. In general, a |
| 1759 | /// scalarized instruction will be represented by VF scalar values in the |
| 1760 | /// vectorized loop, each corresponding to an iteration of the original |
| 1761 | /// scalar loop. |
| 1762 | void collectLoopUniforms(ElementCount VF); |
| 1763 | |
| 1764 | /// Collect the instructions that are scalar after vectorization. An |
| 1765 | /// instruction is scalar if it is known to be uniform or will be scalarized |
| 1766 | /// during vectorization. Non-uniform scalarized instructions will be |
| 1767 | /// represented by VF values in the vectorized loop, each corresponding to an |
| 1768 | /// iteration of the original scalar loop. |
| 1769 | void collectLoopScalars(ElementCount VF); |
| 1770 | |
| 1771 | /// Keeps cost model vectorization decision and cost for instructions. |
| 1772 | /// Right now it is used for memory instructions only. |
| 1773 | using DecisionList = DenseMap<std::pair<Instruction *, ElementCount>, |
| 1774 | std::pair<InstWidening, InstructionCost>>; |
| 1775 | |
| 1776 | DecisionList WideningDecisions; |
| 1777 | |
| 1778 | /// Returns true if \p V is expected to be vectorized and it needs to be |
| 1779 | /// extracted. |
| 1780 | bool (Value *V, ElementCount VF) const { |
| 1781 | Instruction *I = dyn_cast<Instruction>(V); |
| 1782 | if (VF.isScalar() || !I || !TheLoop->contains(I) || |
| 1783 | TheLoop->isLoopInvariant(I)) |
| 1784 | return false; |
| 1785 | |
| 1786 | // Assume we can vectorize V (and hence we need extraction) if the |
| 1787 | // scalars are not computed yet. This can happen, because it is called |
| 1788 | // via getScalarizationOverhead from setCostBasedWideningDecision, before |
| 1789 | // the scalars are collected. That should be a safe assumption in most |
| 1790 | // cases, because we check if the operands have vectorizable types |
| 1791 | // beforehand in LoopVectorizationLegality. |
| 1792 | return Scalars.find(VF) == Scalars.end() || |
| 1793 | !isScalarAfterVectorization(I, VF); |
| 1794 | }; |
| 1795 | |
| 1796 | /// Returns a range containing only operands needing to be extracted. |
| 1797 | SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops, |
| 1798 | ElementCount VF) { |
| 1799 | return SmallVector<Value *, 4>(make_filter_range( |
| 1800 | Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); })); |
| 1801 | } |
| 1802 | |
| 1803 | /// Determines if we have the infrastructure to vectorize loop \p L and its |
| 1804 | /// epilogue, assuming the main loop is vectorized by \p VF. |
| 1805 | bool isCandidateForEpilogueVectorization(const Loop &L, |
| 1806 | const ElementCount VF) const; |
| 1807 | |
| 1808 | /// Returns true if epilogue vectorization is considered profitable, and |
| 1809 | /// false otherwise. |
| 1810 | /// \p VF is the vectorization factor chosen for the original loop. |
| 1811 | bool isEpilogueVectorizationProfitable(const ElementCount VF) const; |
| 1812 | |
| 1813 | public: |
| 1814 | /// The loop that we evaluate. |
| 1815 | Loop *TheLoop; |
| 1816 | |
| 1817 | /// Predicated scalar evolution analysis. |
| 1818 | PredicatedScalarEvolution &PSE; |
| 1819 | |
| 1820 | /// Loop Info analysis. |
| 1821 | LoopInfo *LI; |
| 1822 | |
| 1823 | /// Vectorization legality. |
| 1824 | LoopVectorizationLegality *Legal; |
| 1825 | |
| 1826 | /// Vector target information. |
| 1827 | const TargetTransformInfo &TTI; |
| 1828 | |
| 1829 | /// Target Library Info. |
| 1830 | const TargetLibraryInfo *TLI; |
| 1831 | |
| 1832 | /// Demanded bits analysis. |
| 1833 | DemandedBits *DB; |
| 1834 | |
| 1835 | /// Assumption cache. |
| 1836 | AssumptionCache *AC; |
| 1837 | |
| 1838 | /// Interface to emit optimization remarks. |
| 1839 | OptimizationRemarkEmitter *ORE; |
| 1840 | |
| 1841 | const Function *TheFunction; |
| 1842 | |
| 1843 | /// Loop Vectorize Hint. |
| 1844 | const LoopVectorizeHints *Hints; |
| 1845 | |
| 1846 | /// The interleave access information contains groups of interleaved accesses |
| 1847 | /// with the same stride and close to each other. |
| 1848 | InterleavedAccessInfo &InterleaveInfo; |
| 1849 | |
| 1850 | /// Values to ignore in the cost model. |
| 1851 | SmallPtrSet<const Value *, 16> ValuesToIgnore; |
| 1852 | |
| 1853 | /// Values to ignore in the cost model when VF > 1. |
| 1854 | SmallPtrSet<const Value *, 16> VecValuesToIgnore; |
| 1855 | |
| 1856 | /// Profitable vector factors. |
| 1857 | SmallVector<VectorizationFactor, 8> ProfitableVFs; |
| 1858 | }; |
| 1859 | |
| 1860 | } // end namespace llvm |
| 1861 | |
| 1862 | // Return true if \p OuterLp is an outer loop annotated with hints for explicit |
| 1863 | // vectorization. The loop needs to be annotated with #pragma omp simd |
| 1864 | // simdlen(#) or #pragma clang vectorize(enable) vectorize_width(#). If the |
| 1865 | // vector length information is not provided, vectorization is not considered |
| 1866 | // explicit. Interleave hints are not allowed either. These limitations will be |
| 1867 | // relaxed in the future. |
| 1868 | // Please, note that we are currently forced to abuse the pragma 'clang |
| 1869 | // vectorize' semantics. This pragma provides *auto-vectorization hints* |
| 1870 | // (i.e., LV must check that vectorization is legal) whereas pragma 'omp simd' |
| 1871 | // provides *explicit vectorization hints* (LV can bypass legal checks and |
| 1872 | // assume that vectorization is legal). However, both hints are implemented |
| 1873 | // using the same metadata (llvm.loop.vectorize, processed by |
| 1874 | // LoopVectorizeHints). This will be fixed in the future when the native IR |
| 1875 | // representation for pragma 'omp simd' is introduced. |
| 1876 | static bool (Loop *OuterLp, |
| 1877 | OptimizationRemarkEmitter *ORE) { |
| 1878 | assert(!OuterLp->isInnermost() && "This is not an outer loop" ); |
| 1879 | LoopVectorizeHints Hints(OuterLp, true /*DisableInterleaving*/, *ORE); |
| 1880 | |
| 1881 | // Only outer loops with an explicit vectorization hint are supported. |
| 1882 | // Unannotated outer loops are ignored. |
| 1883 | if (Hints.getForce() == LoopVectorizeHints::FK_Undefined) |
| 1884 | return false; |
| 1885 | |
| 1886 | Function *Fn = OuterLp->getHeader()->getParent(); |
| 1887 | if (!Hints.allowVectorization(Fn, OuterLp, |
| 1888 | true /*VectorizeOnlyWhenForced*/)) { |
| 1889 | LLVM_DEBUG(dbgs() << "LV: Loop hints prevent outer loop vectorization.\n" ); |
| 1890 | return false; |
| 1891 | } |
| 1892 | |
| 1893 | if (Hints.getInterleave() > 1) { |
| 1894 | // TODO: Interleave support is future work. |
| 1895 | LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Interleave is not supported for " |
| 1896 | "outer loops.\n" ); |
| 1897 | Hints.emitRemarkWithHints(); |
| 1898 | return false; |
| 1899 | } |
| 1900 | |
| 1901 | return true; |
| 1902 | } |
| 1903 | |
| 1904 | static void (Loop &L, LoopInfo *LI, |
| 1905 | OptimizationRemarkEmitter *ORE, |
| 1906 | SmallVectorImpl<Loop *> &V) { |
| 1907 | // Collect inner loops and outer loops without irreducible control flow. For |
| 1908 | // now, only collect outer loops that have explicit vectorization hints. If we |
| 1909 | // are stress testing the VPlan H-CFG construction, we collect the outermost |
| 1910 | // loop of every loop nest. |
| 1911 | if (L.isInnermost() || VPlanBuildStressTest || |
| 1912 | (EnableVPlanNativePath && isExplicitVecOuterLoop(&L, ORE))) { |
| 1913 | LoopBlocksRPO RPOT(&L); |
| 1914 | RPOT.perform(LI); |
| 1915 | if (!containsIrreducibleCFG<const BasicBlock *>(RPOT, *LI)) { |
| 1916 | V.push_back(&L); |
| 1917 | // TODO: Collect inner loops inside marked outer loops in case |
| 1918 | // vectorization fails for the outer loop. Do not invoke |
| 1919 | // 'containsIrreducibleCFG' again for inner loops when the outer loop is |
| 1920 | // already known to be reducible. We can use an inherited attribute for |
| 1921 | // that. |
| 1922 | return; |
| 1923 | } |
| 1924 | } |
| 1925 | for (Loop *InnerL : L) |
| 1926 | collectSupportedLoops(*InnerL, LI, ORE, V); |
| 1927 | } |
| 1928 | |
| 1929 | namespace { |
| 1930 | |
| 1931 | /// The LoopVectorize Pass. |
| 1932 | struct LoopVectorize : public FunctionPass { |
| 1933 | /// Pass identification, replacement for typeid |
| 1934 | static char ID; |
| 1935 | |
| 1936 | LoopVectorizePass Impl; |
| 1937 | |
| 1938 | explicit LoopVectorize(bool InterleaveOnlyWhenForced = false, |
| 1939 | bool VectorizeOnlyWhenForced = false) |
| 1940 | : FunctionPass(ID), |
| 1941 | Impl({InterleaveOnlyWhenForced, VectorizeOnlyWhenForced}) { |
| 1942 | initializeLoopVectorizePass(*PassRegistry::getPassRegistry()); |
| 1943 | } |
| 1944 | |
| 1945 | bool runOnFunction(Function &F) override { |
| 1946 | if (skipFunction(F)) |
| 1947 | return false; |
| 1948 | |
| 1949 | auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE(); |
| 1950 | auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo(); |
| 1951 | auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F); |
| 1952 | auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree(); |
| 1953 | auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI(); |
| 1954 | auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>(); |
| 1955 | auto *TLI = TLIP ? &TLIP->getTLI(F) : nullptr; |
| 1956 | auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults(); |
| 1957 | auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F); |
| 1958 | auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>(); |
| 1959 | auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits(); |
| 1960 | auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE(); |
| 1961 | auto *PSI = &getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI(); |
| 1962 | |
| 1963 | std::function<const LoopAccessInfo &(Loop &)> GetLAA = |
| 1964 | [&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); }; |
| 1965 | |
| 1966 | return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC, |
| 1967 | GetLAA, *ORE, PSI).MadeAnyChange; |
| 1968 | } |
| 1969 | |
| 1970 | void getAnalysisUsage(AnalysisUsage &AU) const override { |
| 1971 | AU.addRequired<AssumptionCacheTracker>(); |
| 1972 | AU.addRequired<BlockFrequencyInfoWrapperPass>(); |
| 1973 | AU.addRequired<DominatorTreeWrapperPass>(); |
| 1974 | AU.addRequired<LoopInfoWrapperPass>(); |
| 1975 | AU.addRequired<ScalarEvolutionWrapperPass>(); |
| 1976 | AU.addRequired<TargetTransformInfoWrapperPass>(); |
| 1977 | AU.addRequired<AAResultsWrapperPass>(); |
| 1978 | AU.addRequired<LoopAccessLegacyAnalysis>(); |
| 1979 | AU.addRequired<DemandedBitsWrapperPass>(); |
| 1980 | AU.addRequired<OptimizationRemarkEmitterWrapperPass>(); |
| 1981 | AU.addRequired<InjectTLIMappingsLegacy>(); |
| 1982 | |
| 1983 | // We currently do not preserve loopinfo/dominator analyses with outer loop |
| 1984 | // vectorization. Until this is addressed, mark these analyses as preserved |
| 1985 | // only for non-VPlan-native path. |
| 1986 | // TODO: Preserve Loop and Dominator analyses for VPlan-native path. |
| 1987 | if (!EnableVPlanNativePath) { |
| 1988 | AU.addPreserved<LoopInfoWrapperPass>(); |
| 1989 | AU.addPreserved<DominatorTreeWrapperPass>(); |
| 1990 | } |
| 1991 | |
| 1992 | AU.addPreserved<BasicAAWrapperPass>(); |
| 1993 | AU.addPreserved<GlobalsAAWrapperPass>(); |
| 1994 | AU.addRequired<ProfileSummaryInfoWrapperPass>(); |
| 1995 | } |
| 1996 | }; |
| 1997 | |
| 1998 | } // end anonymous namespace |
| 1999 | |
| 2000 | //===----------------------------------------------------------------------===// |
| 2001 | // Implementation of LoopVectorizationLegality, InnerLoopVectorizer and |
| 2002 | // LoopVectorizationCostModel and LoopVectorizationPlanner. |
| 2003 | //===----------------------------------------------------------------------===// |
| 2004 | |
| 2005 | Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) { |
| 2006 | // We need to place the broadcast of invariant variables outside the loop, |
| 2007 | // but only if it's proven safe to do so. Else, broadcast will be inside |
| 2008 | // vector loop body. |
| 2009 | Instruction *Instr = dyn_cast<Instruction>(V); |
| 2010 | bool SafeToHoist = OrigLoop->isLoopInvariant(V) && |
| 2011 | (!Instr || |
| 2012 | DT->dominates(Instr->getParent(), LoopVectorPreHeader)); |
| 2013 | // Place the code for broadcasting invariant variables in the new preheader. |
| 2014 | IRBuilder<>::InsertPointGuard Guard(Builder); |
| 2015 | if (SafeToHoist) |
| 2016 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 2017 | |
| 2018 | // Broadcast the scalar into all locations in the vector. |
| 2019 | Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast" ); |
| 2020 | |
| 2021 | return Shuf; |
| 2022 | } |
| 2023 | |
| 2024 | void InnerLoopVectorizer::createVectorIntOrFpInductionPHI( |
| 2025 | const InductionDescriptor &II, Value *Step, Value *Start, |
| 2026 | Instruction *EntryVal) { |
| 2027 | assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) && |
| 2028 | "Expected either an induction phi-node or a truncate of it!" ); |
| 2029 | |
| 2030 | // Construct the initial value of the vector IV in the vector loop preheader |
| 2031 | auto CurrIP = Builder.saveIP(); |
| 2032 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 2033 | if (isa<TruncInst>(EntryVal)) { |
| 2034 | assert(Start->getType()->isIntegerTy() && |
| 2035 | "Truncation requires an integer type" ); |
| 2036 | auto *TruncType = cast<IntegerType>(EntryVal->getType()); |
| 2037 | Step = Builder.CreateTrunc(Step, TruncType); |
| 2038 | Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType); |
| 2039 | } |
| 2040 | Value *SplatStart = Builder.CreateVectorSplat(VF, Start); |
| 2041 | Value *SteppedStart = |
| 2042 | getStepVector(SplatStart, 0, Step, II.getInductionOpcode()); |
| 2043 | |
| 2044 | // We create vector phi nodes for both integer and floating-point induction |
| 2045 | // variables. Here, we determine the kind of arithmetic we will perform. |
| 2046 | Instruction::BinaryOps AddOp; |
| 2047 | Instruction::BinaryOps MulOp; |
| 2048 | if (Step->getType()->isIntegerTy()) { |
| 2049 | AddOp = Instruction::Add; |
| 2050 | MulOp = Instruction::Mul; |
| 2051 | } else { |
| 2052 | AddOp = II.getInductionOpcode(); |
| 2053 | MulOp = Instruction::FMul; |
| 2054 | } |
| 2055 | |
| 2056 | // Multiply the vectorization factor by the step using integer or |
| 2057 | // floating-point arithmetic as appropriate. |
| 2058 | Value *ConstVF = |
| 2059 | getSignedIntOrFpConstant(Step->getType(), VF.getKnownMinValue()); |
| 2060 | Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF)); |
| 2061 | |
| 2062 | // Create a vector splat to use in the induction update. |
| 2063 | // |
| 2064 | // FIXME: If the step is non-constant, we create the vector splat with |
| 2065 | // IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't |
| 2066 | // handle a constant vector splat. |
| 2067 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2068 | Value *SplatVF = isa<Constant>(Mul) |
| 2069 | ? ConstantVector::getSplat(VF, cast<Constant>(Mul)) |
| 2070 | : Builder.CreateVectorSplat(VF, Mul); |
| 2071 | Builder.restoreIP(CurrIP); |
| 2072 | |
| 2073 | // We may need to add the step a number of times, depending on the unroll |
| 2074 | // factor. The last of those goes into the PHI. |
| 2075 | PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind" , |
| 2076 | &*LoopVectorBody->getFirstInsertionPt()); |
| 2077 | VecInd->setDebugLoc(EntryVal->getDebugLoc()); |
| 2078 | Instruction *LastInduction = VecInd; |
| 2079 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2080 | VectorLoopValueMap.setVectorValue(EntryVal, Part, LastInduction); |
| 2081 | |
| 2082 | if (isa<TruncInst>(EntryVal)) |
| 2083 | addMetadata(LastInduction, EntryVal); |
| 2084 | recordVectorLoopValueForInductionCast(II, EntryVal, LastInduction, Part); |
| 2085 | |
| 2086 | LastInduction = cast<Instruction>(addFastMathFlag( |
| 2087 | Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add" ))); |
| 2088 | LastInduction->setDebugLoc(EntryVal->getDebugLoc()); |
| 2089 | } |
| 2090 | |
| 2091 | // Move the last step to the end of the latch block. This ensures consistent |
| 2092 | // placement of all induction updates. |
| 2093 | auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| 2094 | auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator()); |
| 2095 | auto *ICmp = cast<Instruction>(Br->getCondition()); |
| 2096 | LastInduction->moveBefore(ICmp); |
| 2097 | LastInduction->setName("vec.ind.next" ); |
| 2098 | |
| 2099 | VecInd->addIncoming(SteppedStart, LoopVectorPreHeader); |
| 2100 | VecInd->addIncoming(LastInduction, LoopVectorLatch); |
| 2101 | } |
| 2102 | |
| 2103 | bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const { |
| 2104 | return Cost->isScalarAfterVectorization(I, VF) || |
| 2105 | Cost->isProfitableToScalarize(I, VF); |
| 2106 | } |
| 2107 | |
| 2108 | bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const { |
| 2109 | if (shouldScalarizeInstruction(IV)) |
| 2110 | return true; |
| 2111 | auto isScalarInst = [&](User *U) -> bool { |
| 2112 | auto *I = cast<Instruction>(U); |
| 2113 | return (OrigLoop->contains(I) && shouldScalarizeInstruction(I)); |
| 2114 | }; |
| 2115 | return llvm::any_of(IV->users(), isScalarInst); |
| 2116 | } |
| 2117 | |
| 2118 | void InnerLoopVectorizer::recordVectorLoopValueForInductionCast( |
| 2119 | const InductionDescriptor &ID, const Instruction *EntryVal, |
| 2120 | Value *VectorLoopVal, unsigned Part, unsigned Lane) { |
| 2121 | assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) && |
| 2122 | "Expected either an induction phi-node or a truncate of it!" ); |
| 2123 | |
| 2124 | // This induction variable is not the phi from the original loop but the |
| 2125 | // newly-created IV based on the proof that casted Phi is equal to the |
| 2126 | // uncasted Phi in the vectorized loop (under a runtime guard possibly). It |
| 2127 | // re-uses the same InductionDescriptor that original IV uses but we don't |
| 2128 | // have to do any recording in this case - that is done when original IV is |
| 2129 | // processed. |
| 2130 | if (isa<TruncInst>(EntryVal)) |
| 2131 | return; |
| 2132 | |
| 2133 | const SmallVectorImpl<Instruction *> &Casts = ID.getCastInsts(); |
| 2134 | if (Casts.empty()) |
| 2135 | return; |
| 2136 | // Only the first Cast instruction in the Casts vector is of interest. |
| 2137 | // The rest of the Casts (if exist) have no uses outside the |
| 2138 | // induction update chain itself. |
| 2139 | Instruction *CastInst = *Casts.begin(); |
| 2140 | if (Lane < UINT_MAX) |
| 2141 | VectorLoopValueMap.setScalarValue(CastInst, {Part, Lane}, VectorLoopVal); |
| 2142 | else |
| 2143 | VectorLoopValueMap.setVectorValue(CastInst, Part, VectorLoopVal); |
| 2144 | } |
| 2145 | |
| 2146 | void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, Value *Start, |
| 2147 | TruncInst *Trunc) { |
| 2148 | assert((IV->getType()->isIntegerTy() || IV != OldInduction) && |
| 2149 | "Primary induction variable must have an integer type" ); |
| 2150 | |
| 2151 | auto II = Legal->getInductionVars().find(IV); |
| 2152 | assert(II != Legal->getInductionVars().end() && "IV is not an induction" ); |
| 2153 | |
| 2154 | auto ID = II->second; |
| 2155 | assert(IV->getType() == ID.getStartValue()->getType() && "Types must match" ); |
| 2156 | |
| 2157 | // The value from the original loop to which we are mapping the new induction |
| 2158 | // variable. |
| 2159 | Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV; |
| 2160 | |
| 2161 | auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 2162 | |
| 2163 | // Generate code for the induction step. Note that induction steps are |
| 2164 | // required to be loop-invariant |
| 2165 | auto CreateStepValue = [&](const SCEV *Step) -> Value * { |
| 2166 | assert(PSE.getSE()->isLoopInvariant(Step, OrigLoop) && |
| 2167 | "Induction step should be loop invariant" ); |
| 2168 | if (PSE.getSE()->isSCEVable(IV->getType())) { |
| 2169 | SCEVExpander Exp(*PSE.getSE(), DL, "induction" ); |
| 2170 | return Exp.expandCodeFor(Step, Step->getType(), |
| 2171 | LoopVectorPreHeader->getTerminator()); |
| 2172 | } |
| 2173 | return cast<SCEVUnknown>(Step)->getValue(); |
| 2174 | }; |
| 2175 | |
| 2176 | // The scalar value to broadcast. This is derived from the canonical |
| 2177 | // induction variable. If a truncation type is given, truncate the canonical |
| 2178 | // induction variable and step. Otherwise, derive these values from the |
| 2179 | // induction descriptor. |
| 2180 | auto CreateScalarIV = [&](Value *&Step) -> Value * { |
| 2181 | Value *ScalarIV = Induction; |
| 2182 | if (IV != OldInduction) { |
| 2183 | ScalarIV = IV->getType()->isIntegerTy() |
| 2184 | ? Builder.CreateSExtOrTrunc(Induction, IV->getType()) |
| 2185 | : Builder.CreateCast(Instruction::SIToFP, Induction, |
| 2186 | IV->getType()); |
| 2187 | ScalarIV = emitTransformedIndex(Builder, ScalarIV, PSE.getSE(), DL, ID); |
| 2188 | ScalarIV->setName("offset.idx" ); |
| 2189 | } |
| 2190 | if (Trunc) { |
| 2191 | auto *TruncType = cast<IntegerType>(Trunc->getType()); |
| 2192 | assert(Step->getType()->isIntegerTy() && |
| 2193 | "Truncation requires an integer step" ); |
| 2194 | ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType); |
| 2195 | Step = Builder.CreateTrunc(Step, TruncType); |
| 2196 | } |
| 2197 | return ScalarIV; |
| 2198 | }; |
| 2199 | |
| 2200 | // Create the vector values from the scalar IV, in the absence of creating a |
| 2201 | // vector IV. |
| 2202 | auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) { |
| 2203 | Value *Broadcasted = getBroadcastInstrs(ScalarIV); |
| 2204 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2205 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2206 | Value *EntryPart = |
| 2207 | getStepVector(Broadcasted, VF.getKnownMinValue() * Part, Step, |
| 2208 | ID.getInductionOpcode()); |
| 2209 | VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart); |
| 2210 | if (Trunc) |
| 2211 | addMetadata(EntryPart, Trunc); |
| 2212 | recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, Part); |
| 2213 | } |
| 2214 | }; |
| 2215 | |
| 2216 | // Now do the actual transformations, and start with creating the step value. |
| 2217 | Value *Step = CreateStepValue(ID.getStep()); |
| 2218 | if (VF.isZero() || VF.isScalar()) { |
| 2219 | Value *ScalarIV = CreateScalarIV(Step); |
| 2220 | CreateSplatIV(ScalarIV, Step); |
| 2221 | return; |
| 2222 | } |
| 2223 | |
| 2224 | // Determine if we want a scalar version of the induction variable. This is |
| 2225 | // true if the induction variable itself is not widened, or if it has at |
| 2226 | // least one user in the loop that is not widened. |
| 2227 | auto NeedsScalarIV = needsScalarInduction(EntryVal); |
| 2228 | if (!NeedsScalarIV) { |
| 2229 | createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal); |
| 2230 | return; |
| 2231 | } |
| 2232 | |
| 2233 | // Try to create a new independent vector induction variable. If we can't |
| 2234 | // create the phi node, we will splat the scalar induction variable in each |
| 2235 | // loop iteration. |
| 2236 | if (!shouldScalarizeInstruction(EntryVal)) { |
| 2237 | createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal); |
| 2238 | Value *ScalarIV = CreateScalarIV(Step); |
| 2239 | // Create scalar steps that can be used by instructions we will later |
| 2240 | // scalarize. Note that the addition of the scalar steps will not increase |
| 2241 | // the number of instructions in the loop in the common case prior to |
| 2242 | // InstCombine. We will be trading one vector extract for each scalar step. |
| 2243 | buildScalarSteps(ScalarIV, Step, EntryVal, ID); |
| 2244 | return; |
| 2245 | } |
| 2246 | |
| 2247 | // All IV users are scalar instructions, so only emit a scalar IV, not a |
| 2248 | // vectorised IV. Except when we tail-fold, then the splat IV feeds the |
| 2249 | // predicate used by the masked loads/stores. |
| 2250 | Value *ScalarIV = CreateScalarIV(Step); |
| 2251 | if (!Cost->isScalarEpilogueAllowed()) |
| 2252 | CreateSplatIV(ScalarIV, Step); |
| 2253 | buildScalarSteps(ScalarIV, Step, EntryVal, ID); |
| 2254 | } |
| 2255 | |
| 2256 | Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step, |
| 2257 | Instruction::BinaryOps BinOp) { |
| 2258 | // Create and check the types. |
| 2259 | auto *ValVTy = cast<FixedVectorType>(Val->getType()); |
| 2260 | int VLen = ValVTy->getNumElements(); |
| 2261 | |
| 2262 | Type *STy = Val->getType()->getScalarType(); |
| 2263 | assert((STy->isIntegerTy() || STy->isFloatingPointTy()) && |
| 2264 | "Induction Step must be an integer or FP" ); |
| 2265 | assert(Step->getType() == STy && "Step has wrong type" ); |
| 2266 | |
| 2267 | SmallVector<Constant *, 8> Indices; |
| 2268 | |
| 2269 | if (STy->isIntegerTy()) { |
| 2270 | // Create a vector of consecutive numbers from zero to VF. |
| 2271 | for (int i = 0; i < VLen; ++i) |
| 2272 | Indices.push_back(ConstantInt::get(STy, StartIdx + i)); |
| 2273 | |
| 2274 | // Add the consecutive indices to the vector value. |
| 2275 | Constant *Cv = ConstantVector::get(Indices); |
| 2276 | assert(Cv->getType() == Val->getType() && "Invalid consecutive vec" ); |
| 2277 | Step = Builder.CreateVectorSplat(VLen, Step); |
| 2278 | assert(Step->getType() == Val->getType() && "Invalid step vec" ); |
| 2279 | // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| 2280 | // which can be found from the original scalar operations. |
| 2281 | Step = Builder.CreateMul(Cv, Step); |
| 2282 | return Builder.CreateAdd(Val, Step, "induction" ); |
| 2283 | } |
| 2284 | |
| 2285 | // Floating point induction. |
| 2286 | assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) && |
| 2287 | "Binary Opcode should be specified for FP induction" ); |
| 2288 | // Create a vector of consecutive numbers from zero to VF. |
| 2289 | for (int i = 0; i < VLen; ++i) |
| 2290 | Indices.push_back(ConstantFP::get(STy, (double)(StartIdx + i))); |
| 2291 | |
| 2292 | // Add the consecutive indices to the vector value. |
| 2293 | Constant *Cv = ConstantVector::get(Indices); |
| 2294 | |
| 2295 | Step = Builder.CreateVectorSplat(VLen, Step); |
| 2296 | |
| 2297 | // Floating point operations had to be 'fast' to enable the induction. |
| 2298 | FastMathFlags Flags; |
| 2299 | Flags.setFast(); |
| 2300 | |
| 2301 | Value *MulOp = Builder.CreateFMul(Cv, Step); |
| 2302 | if (isa<Instruction>(MulOp)) |
| 2303 | // Have to check, MulOp may be a constant |
| 2304 | cast<Instruction>(MulOp)->setFastMathFlags(Flags); |
| 2305 | |
| 2306 | Value *BOp = Builder.CreateBinOp(BinOp, Val, MulOp, "induction" ); |
| 2307 | if (isa<Instruction>(BOp)) |
| 2308 | cast<Instruction>(BOp)->setFastMathFlags(Flags); |
| 2309 | return BOp; |
| 2310 | } |
| 2311 | |
| 2312 | void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step, |
| 2313 | Instruction *EntryVal, |
| 2314 | const InductionDescriptor &ID) { |
| 2315 | // We shouldn't have to build scalar steps if we aren't vectorizing. |
| 2316 | assert(VF.isVector() && "VF should be greater than one" ); |
| 2317 | // Get the value type and ensure it and the step have the same integer type. |
| 2318 | Type *ScalarIVTy = ScalarIV->getType()->getScalarType(); |
| 2319 | assert(ScalarIVTy == Step->getType() && |
| 2320 | "Val and Step should have the same type" ); |
| 2321 | |
| 2322 | // We build scalar steps for both integer and floating-point induction |
| 2323 | // variables. Here, we determine the kind of arithmetic we will perform. |
| 2324 | Instruction::BinaryOps AddOp; |
| 2325 | Instruction::BinaryOps MulOp; |
| 2326 | if (ScalarIVTy->isIntegerTy()) { |
| 2327 | AddOp = Instruction::Add; |
| 2328 | MulOp = Instruction::Mul; |
| 2329 | } else { |
| 2330 | AddOp = ID.getInductionOpcode(); |
| 2331 | MulOp = Instruction::FMul; |
| 2332 | } |
| 2333 | |
| 2334 | // Determine the number of scalars we need to generate for each unroll |
| 2335 | // iteration. If EntryVal is uniform, we only need to generate the first |
| 2336 | // lane. Otherwise, we generate all VF values. |
| 2337 | unsigned Lanes = |
| 2338 | Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF) |
| 2339 | ? 1 |
| 2340 | : VF.getKnownMinValue(); |
| 2341 | assert((!VF.isScalable() || Lanes == 1) && |
| 2342 | "Should never scalarize a scalable vector" ); |
| 2343 | // Compute the scalar steps and save the results in VectorLoopValueMap. |
| 2344 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2345 | for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| 2346 | auto *IntStepTy = IntegerType::get(ScalarIVTy->getContext(), |
| 2347 | ScalarIVTy->getScalarSizeInBits()); |
| 2348 | Value *StartIdx = |
| 2349 | createStepForVF(Builder, ConstantInt::get(IntStepTy, Part), VF); |
| 2350 | if (ScalarIVTy->isFloatingPointTy()) |
| 2351 | StartIdx = Builder.CreateSIToFP(StartIdx, ScalarIVTy); |
| 2352 | StartIdx = addFastMathFlag(Builder.CreateBinOp( |
| 2353 | AddOp, StartIdx, getSignedIntOrFpConstant(ScalarIVTy, Lane))); |
| 2354 | // The step returned by `createStepForVF` is a runtime-evaluated value |
| 2355 | // when VF is scalable. Otherwise, it should be folded into a Constant. |
| 2356 | assert((VF.isScalable() || isa<Constant>(StartIdx)) && |
| 2357 | "Expected StartIdx to be folded to a constant when VF is not " |
| 2358 | "scalable" ); |
| 2359 | auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step)); |
| 2360 | auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul)); |
| 2361 | VectorLoopValueMap.setScalarValue(EntryVal, {Part, Lane}, Add); |
| 2362 | recordVectorLoopValueForInductionCast(ID, EntryVal, Add, Part, Lane); |
| 2363 | } |
| 2364 | } |
| 2365 | } |
| 2366 | |
| 2367 | Value *InnerLoopVectorizer::getOrCreateVectorValue(Value *V, unsigned Part) { |
| 2368 | assert(V != Induction && "The new induction variable should not be used." ); |
| 2369 | assert(!V->getType()->isVectorTy() && "Can't widen a vector" ); |
| 2370 | assert(!V->getType()->isVoidTy() && "Type does not produce a value" ); |
| 2371 | |
| 2372 | // If we have a stride that is replaced by one, do it here. Defer this for |
| 2373 | // the VPlan-native path until we start running Legal checks in that path. |
| 2374 | if (!EnableVPlanNativePath && Legal->hasStride(V)) |
| 2375 | V = ConstantInt::get(V->getType(), 1); |
| 2376 | |
| 2377 | // If we have a vector mapped to this value, return it. |
| 2378 | if (VectorLoopValueMap.hasVectorValue(V, Part)) |
| 2379 | return VectorLoopValueMap.getVectorValue(V, Part); |
| 2380 | |
| 2381 | // If the value has not been vectorized, check if it has been scalarized |
| 2382 | // instead. If it has been scalarized, and we actually need the value in |
| 2383 | // vector form, we will construct the vector values on demand. |
| 2384 | if (VectorLoopValueMap.hasAnyScalarValue(V)) { |
| 2385 | Value *ScalarValue = VectorLoopValueMap.getScalarValue(V, {Part, 0}); |
| 2386 | |
| 2387 | // If we've scalarized a value, that value should be an instruction. |
| 2388 | auto *I = cast<Instruction>(V); |
| 2389 | |
| 2390 | // If we aren't vectorizing, we can just copy the scalar map values over to |
| 2391 | // the vector map. |
| 2392 | if (VF.isScalar()) { |
| 2393 | VectorLoopValueMap.setVectorValue(V, Part, ScalarValue); |
| 2394 | return ScalarValue; |
| 2395 | } |
| 2396 | |
| 2397 | // Get the last scalar instruction we generated for V and Part. If the value |
| 2398 | // is known to be uniform after vectorization, this corresponds to lane zero |
| 2399 | // of the Part unroll iteration. Otherwise, the last instruction is the one |
| 2400 | // we created for the last vector lane of the Part unroll iteration. |
| 2401 | unsigned LastLane = Cost->isUniformAfterVectorization(I, VF) |
| 2402 | ? 0 |
| 2403 | : VF.getKnownMinValue() - 1; |
| 2404 | assert((!VF.isScalable() || LastLane == 0) && |
| 2405 | "Scalable vectorization can't lead to any scalarized values." ); |
| 2406 | auto *LastInst = cast<Instruction>( |
| 2407 | VectorLoopValueMap.getScalarValue(V, {Part, LastLane})); |
| 2408 | |
| 2409 | // Set the insert point after the last scalarized instruction. This ensures |
| 2410 | // the insertelement sequence will directly follow the scalar definitions. |
| 2411 | auto OldIP = Builder.saveIP(); |
| 2412 | auto NewIP = std::next(BasicBlock::iterator(LastInst)); |
| 2413 | Builder.SetInsertPoint(&*NewIP); |
| 2414 | |
| 2415 | // However, if we are vectorizing, we need to construct the vector values. |
| 2416 | // If the value is known to be uniform after vectorization, we can just |
| 2417 | // broadcast the scalar value corresponding to lane zero for each unroll |
| 2418 | // iteration. Otherwise, we construct the vector values using insertelement |
| 2419 | // instructions. Since the resulting vectors are stored in |
| 2420 | // VectorLoopValueMap, we will only generate the insertelements once. |
| 2421 | Value *VectorValue = nullptr; |
| 2422 | if (Cost->isUniformAfterVectorization(I, VF)) { |
| 2423 | VectorValue = getBroadcastInstrs(ScalarValue); |
| 2424 | VectorLoopValueMap.setVectorValue(V, Part, VectorValue); |
| 2425 | } else { |
| 2426 | // Initialize packing with insertelements to start from poison. |
| 2427 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 2428 | Value *Poison = PoisonValue::get(VectorType::get(V->getType(), VF)); |
| 2429 | VectorLoopValueMap.setVectorValue(V, Part, Poison); |
| 2430 | for (unsigned Lane = 0; Lane < VF.getKnownMinValue(); ++Lane) |
| 2431 | packScalarIntoVectorValue(V, {Part, Lane}); |
| 2432 | VectorValue = VectorLoopValueMap.getVectorValue(V, Part); |
| 2433 | } |
| 2434 | Builder.restoreIP(OldIP); |
| 2435 | return VectorValue; |
| 2436 | } |
| 2437 | |
| 2438 | // If this scalar is unknown, assume that it is a constant or that it is |
| 2439 | // loop invariant. Broadcast V and save the value for future uses. |
| 2440 | Value *B = getBroadcastInstrs(V); |
| 2441 | VectorLoopValueMap.setVectorValue(V, Part, B); |
| 2442 | return B; |
| 2443 | } |
| 2444 | |
| 2445 | Value * |
| 2446 | InnerLoopVectorizer::getOrCreateScalarValue(Value *V, |
| 2447 | const VPIteration &Instance) { |
| 2448 | // If the value is not an instruction contained in the loop, it should |
| 2449 | // already be scalar. |
| 2450 | if (OrigLoop->isLoopInvariant(V)) |
| 2451 | return V; |
| 2452 | |
| 2453 | assert(Instance.Lane > 0 |
| 2454 | ? !Cost->isUniformAfterVectorization(cast<Instruction>(V), VF) |
| 2455 | : true && "Uniform values only have lane zero" ); |
| 2456 | |
| 2457 | // If the value from the original loop has not been vectorized, it is |
| 2458 | // represented by UF x VF scalar values in the new loop. Return the requested |
| 2459 | // scalar value. |
| 2460 | if (VectorLoopValueMap.hasScalarValue(V, Instance)) |
| 2461 | return VectorLoopValueMap.getScalarValue(V, Instance); |
| 2462 | |
| 2463 | // If the value has not been scalarized, get its entry in VectorLoopValueMap |
| 2464 | // for the given unroll part. If this entry is not a vector type (i.e., the |
| 2465 | // vectorization factor is one), there is no need to generate an |
| 2466 | // extractelement instruction. |
| 2467 | auto *U = getOrCreateVectorValue(V, Instance.Part); |
| 2468 | if (!U->getType()->isVectorTy()) { |
| 2469 | assert(VF.isScalar() && "Value not scalarized has non-vector type" ); |
| 2470 | return U; |
| 2471 | } |
| 2472 | |
| 2473 | // Otherwise, the value from the original loop has been vectorized and is |
| 2474 | // represented by UF vector values. Extract and return the requested scalar |
| 2475 | // value from the appropriate vector lane. |
| 2476 | return Builder.CreateExtractElement(U, Builder.getInt32(Instance.Lane)); |
| 2477 | } |
| 2478 | |
| 2479 | void InnerLoopVectorizer::packScalarIntoVectorValue( |
| 2480 | Value *V, const VPIteration &Instance) { |
| 2481 | assert(V != Induction && "The new induction variable should not be used." ); |
| 2482 | assert(!V->getType()->isVectorTy() && "Can't pack a vector" ); |
| 2483 | assert(!V->getType()->isVoidTy() && "Type does not produce a value" ); |
| 2484 | |
| 2485 | Value *ScalarInst = VectorLoopValueMap.getScalarValue(V, Instance); |
| 2486 | Value *VectorValue = VectorLoopValueMap.getVectorValue(V, Instance.Part); |
| 2487 | VectorValue = Builder.CreateInsertElement(VectorValue, ScalarInst, |
| 2488 | Builder.getInt32(Instance.Lane)); |
| 2489 | VectorLoopValueMap.resetVectorValue(V, Instance.Part, VectorValue); |
| 2490 | } |
| 2491 | |
| 2492 | Value *InnerLoopVectorizer::reverseVector(Value *Vec) { |
| 2493 | assert(Vec->getType()->isVectorTy() && "Invalid type" ); |
| 2494 | assert(!VF.isScalable() && "Cannot reverse scalable vectors" ); |
| 2495 | SmallVector<int, 8> ShuffleMask; |
| 2496 | for (unsigned i = 0; i < VF.getKnownMinValue(); ++i) |
| 2497 | ShuffleMask.push_back(VF.getKnownMinValue() - i - 1); |
| 2498 | |
| 2499 | return Builder.CreateShuffleVector(Vec, ShuffleMask, "reverse" ); |
| 2500 | } |
| 2501 | |
| 2502 | // Return whether we allow using masked interleave-groups (for dealing with |
| 2503 | // strided loads/stores that reside in predicated blocks, or for dealing |
| 2504 | // with gaps). |
| 2505 | static bool useMaskedInterleavedAccesses(const TargetTransformInfo &TTI) { |
| 2506 | // If an override option has been passed in for interleaved accesses, use it. |
| 2507 | if (EnableMaskedInterleavedMemAccesses.getNumOccurrences() > 0) |
| 2508 | return EnableMaskedInterleavedMemAccesses; |
| 2509 | |
| 2510 | return TTI.enableMaskedInterleavedAccessVectorization(); |
| 2511 | } |
| 2512 | |
| 2513 | // Try to vectorize the interleave group that \p Instr belongs to. |
| 2514 | // |
| 2515 | // E.g. Translate following interleaved load group (factor = 3): |
| 2516 | // for (i = 0; i < N; i+=3) { |
| 2517 | // R = Pic[i]; // Member of index 0 |
| 2518 | // G = Pic[i+1]; // Member of index 1 |
| 2519 | // B = Pic[i+2]; // Member of index 2 |
| 2520 | // ... // do something to R, G, B |
| 2521 | // } |
| 2522 | // To: |
| 2523 | // %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B |
| 2524 | // %R.vec = shuffle %wide.vec, poison, <0, 3, 6, 9> ; R elements |
| 2525 | // %G.vec = shuffle %wide.vec, poison, <1, 4, 7, 10> ; G elements |
| 2526 | // %B.vec = shuffle %wide.vec, poison, <2, 5, 8, 11> ; B elements |
| 2527 | // |
| 2528 | // Or translate following interleaved store group (factor = 3): |
| 2529 | // for (i = 0; i < N; i+=3) { |
| 2530 | // ... do something to R, G, B |
| 2531 | // Pic[i] = R; // Member of index 0 |
| 2532 | // Pic[i+1] = G; // Member of index 1 |
| 2533 | // Pic[i+2] = B; // Member of index 2 |
| 2534 | // } |
| 2535 | // To: |
| 2536 | // %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7> |
| 2537 | // %B_U.vec = shuffle %B.vec, poison, <0, 1, 2, 3, u, u, u, u> |
| 2538 | // %interleaved.vec = shuffle %R_G.vec, %B_U.vec, |
| 2539 | // <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements |
| 2540 | // store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B |
| 2541 | void InnerLoopVectorizer::vectorizeInterleaveGroup( |
| 2542 | const InterleaveGroup<Instruction> *Group, ArrayRef<VPValue *> VPDefs, |
| 2543 | VPTransformState &State, VPValue *Addr, ArrayRef<VPValue *> StoredValues, |
| 2544 | VPValue *BlockInMask) { |
| 2545 | Instruction *Instr = Group->getInsertPos(); |
| 2546 | const DataLayout &DL = Instr->getModule()->getDataLayout(); |
| 2547 | |
| 2548 | // Prepare for the vector type of the interleaved load/store. |
| 2549 | Type *ScalarTy = getMemInstValueType(Instr); |
| 2550 | unsigned InterleaveFactor = Group->getFactor(); |
| 2551 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2552 | auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor); |
| 2553 | |
| 2554 | // Prepare for the new pointers. |
| 2555 | SmallVector<Value *, 2> AddrParts; |
| 2556 | unsigned Index = Group->getIndex(Instr); |
| 2557 | |
| 2558 | // TODO: extend the masked interleaved-group support to reversed access. |
| 2559 | assert((!BlockInMask || !Group->isReverse()) && |
| 2560 | "Reversed masked interleave-group not supported." ); |
| 2561 | |
| 2562 | // If the group is reverse, adjust the index to refer to the last vector lane |
| 2563 | // instead of the first. We adjust the index from the first vector lane, |
| 2564 | // rather than directly getting the pointer for lane VF - 1, because the |
| 2565 | // pointer operand of the interleaved access is supposed to be uniform. For |
| 2566 | // uniform instructions, we're only required to generate a value for the |
| 2567 | // first vector lane in each unroll iteration. |
| 2568 | assert(!VF.isScalable() && |
| 2569 | "scalable vector reverse operation is not implemented" ); |
| 2570 | if (Group->isReverse()) |
| 2571 | Index += (VF.getKnownMinValue() - 1) * Group->getFactor(); |
| 2572 | |
| 2573 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2574 | Value *AddrPart = State.get(Addr, {Part, 0}); |
| 2575 | setDebugLocFromInst(Builder, AddrPart); |
| 2576 | |
| 2577 | // Notice current instruction could be any index. Need to adjust the address |
| 2578 | // to the member of index 0. |
| 2579 | // |
| 2580 | // E.g. a = A[i+1]; // Member of index 1 (Current instruction) |
| 2581 | // b = A[i]; // Member of index 0 |
| 2582 | // Current pointer is pointed to A[i+1], adjust it to A[i]. |
| 2583 | // |
| 2584 | // E.g. A[i+1] = a; // Member of index 1 |
| 2585 | // A[i] = b; // Member of index 0 |
| 2586 | // A[i+2] = c; // Member of index 2 (Current instruction) |
| 2587 | // Current pointer is pointed to A[i+2], adjust it to A[i]. |
| 2588 | |
| 2589 | bool InBounds = false; |
| 2590 | if (auto *gep = dyn_cast<GetElementPtrInst>(AddrPart->stripPointerCasts())) |
| 2591 | InBounds = gep->isInBounds(); |
| 2592 | AddrPart = Builder.CreateGEP(ScalarTy, AddrPart, Builder.getInt32(-Index)); |
| 2593 | cast<GetElementPtrInst>(AddrPart)->setIsInBounds(InBounds); |
| 2594 | |
| 2595 | // Cast to the vector pointer type. |
| 2596 | unsigned AddressSpace = AddrPart->getType()->getPointerAddressSpace(); |
| 2597 | Type *PtrTy = VecTy->getPointerTo(AddressSpace); |
| 2598 | AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy)); |
| 2599 | } |
| 2600 | |
| 2601 | setDebugLocFromInst(Builder, Instr); |
| 2602 | Value *PoisonVec = PoisonValue::get(VecTy); |
| 2603 | |
| 2604 | Value *MaskForGaps = nullptr; |
| 2605 | if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) { |
| 2606 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2607 | MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group); |
| 2608 | assert(MaskForGaps && "Mask for Gaps is required but it is null" ); |
| 2609 | } |
| 2610 | |
| 2611 | // Vectorize the interleaved load group. |
| 2612 | if (isa<LoadInst>(Instr)) { |
| 2613 | // For each unroll part, create a wide load for the group. |
| 2614 | SmallVector<Value *, 2> NewLoads; |
| 2615 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2616 | Instruction *NewLoad; |
| 2617 | if (BlockInMask || MaskForGaps) { |
| 2618 | assert(useMaskedInterleavedAccesses(*TTI) && |
| 2619 | "masked interleaved groups are not allowed." ); |
| 2620 | Value *GroupMask = MaskForGaps; |
| 2621 | if (BlockInMask) { |
| 2622 | Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| 2623 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2624 | Value *ShuffledMask = Builder.CreateShuffleVector( |
| 2625 | BlockInMaskPart, |
| 2626 | createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| 2627 | "interleaved.mask" ); |
| 2628 | GroupMask = MaskForGaps |
| 2629 | ? Builder.CreateBinOp(Instruction::And, ShuffledMask, |
| 2630 | MaskForGaps) |
| 2631 | : ShuffledMask; |
| 2632 | } |
| 2633 | NewLoad = |
| 2634 | Builder.CreateMaskedLoad(AddrParts[Part], Group->getAlign(), |
| 2635 | GroupMask, PoisonVec, "wide.masked.vec" ); |
| 2636 | } |
| 2637 | else |
| 2638 | NewLoad = Builder.CreateAlignedLoad(VecTy, AddrParts[Part], |
| 2639 | Group->getAlign(), "wide.vec" ); |
| 2640 | Group->addMetadata(NewLoad); |
| 2641 | NewLoads.push_back(NewLoad); |
| 2642 | } |
| 2643 | |
| 2644 | // For each member in the group, shuffle out the appropriate data from the |
| 2645 | // wide loads. |
| 2646 | unsigned J = 0; |
| 2647 | for (unsigned I = 0; I < InterleaveFactor; ++I) { |
| 2648 | Instruction *Member = Group->getMember(I); |
| 2649 | |
| 2650 | // Skip the gaps in the group. |
| 2651 | if (!Member) |
| 2652 | continue; |
| 2653 | |
| 2654 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2655 | auto StrideMask = |
| 2656 | createStrideMask(I, InterleaveFactor, VF.getKnownMinValue()); |
| 2657 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2658 | Value *StridedVec = Builder.CreateShuffleVector( |
| 2659 | NewLoads[Part], StrideMask, "strided.vec" ); |
| 2660 | |
| 2661 | // If this member has different type, cast the result type. |
| 2662 | if (Member->getType() != ScalarTy) { |
| 2663 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 2664 | VectorType *OtherVTy = VectorType::get(Member->getType(), VF); |
| 2665 | StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL); |
| 2666 | } |
| 2667 | |
| 2668 | if (Group->isReverse()) |
| 2669 | StridedVec = reverseVector(StridedVec); |
| 2670 | |
| 2671 | State.set(VPDefs[J], Member, StridedVec, Part); |
| 2672 | } |
| 2673 | ++J; |
| 2674 | } |
| 2675 | return; |
| 2676 | } |
| 2677 | |
| 2678 | // The sub vector type for current instruction. |
| 2679 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 2680 | auto *SubVT = VectorType::get(ScalarTy, VF); |
| 2681 | |
| 2682 | // Vectorize the interleaved store group. |
| 2683 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2684 | // Collect the stored vector from each member. |
| 2685 | SmallVector<Value *, 4> StoredVecs; |
| 2686 | for (unsigned i = 0; i < InterleaveFactor; i++) { |
| 2687 | // Interleaved store group doesn't allow a gap, so each index has a member |
| 2688 | assert(Group->getMember(i) && "Fail to get a member from an interleaved store group" ); |
| 2689 | |
| 2690 | Value *StoredVec = State.get(StoredValues[i], Part); |
| 2691 | |
| 2692 | if (Group->isReverse()) |
| 2693 | StoredVec = reverseVector(StoredVec); |
| 2694 | |
| 2695 | // If this member has different type, cast it to a unified type. |
| 2696 | |
| 2697 | if (StoredVec->getType() != SubVT) |
| 2698 | StoredVec = createBitOrPointerCast(StoredVec, SubVT, DL); |
| 2699 | |
| 2700 | StoredVecs.push_back(StoredVec); |
| 2701 | } |
| 2702 | |
| 2703 | // Concatenate all vectors into a wide vector. |
| 2704 | Value *WideVec = concatenateVectors(Builder, StoredVecs); |
| 2705 | |
| 2706 | // Interleave the elements in the wide vector. |
| 2707 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 2708 | Value *IVec = Builder.CreateShuffleVector( |
| 2709 | WideVec, createInterleaveMask(VF.getKnownMinValue(), InterleaveFactor), |
| 2710 | "interleaved.vec" ); |
| 2711 | |
| 2712 | Instruction *NewStoreInstr; |
| 2713 | if (BlockInMask) { |
| 2714 | Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| 2715 | Value *ShuffledMask = Builder.CreateShuffleVector( |
| 2716 | BlockInMaskPart, |
| 2717 | createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| 2718 | "interleaved.mask" ); |
| 2719 | NewStoreInstr = Builder.CreateMaskedStore( |
| 2720 | IVec, AddrParts[Part], Group->getAlign(), ShuffledMask); |
| 2721 | } |
| 2722 | else |
| 2723 | NewStoreInstr = |
| 2724 | Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign()); |
| 2725 | |
| 2726 | Group->addMetadata(NewStoreInstr); |
| 2727 | } |
| 2728 | } |
| 2729 | |
| 2730 | void InnerLoopVectorizer::vectorizeMemoryInstruction( |
| 2731 | Instruction *Instr, VPTransformState &State, VPValue *Def, VPValue *Addr, |
| 2732 | VPValue *StoredValue, VPValue *BlockInMask) { |
| 2733 | // Attempt to issue a wide load. |
| 2734 | LoadInst *LI = dyn_cast<LoadInst>(Instr); |
| 2735 | StoreInst *SI = dyn_cast<StoreInst>(Instr); |
| 2736 | |
| 2737 | assert((LI || SI) && "Invalid Load/Store instruction" ); |
| 2738 | assert((!SI || StoredValue) && "No stored value provided for widened store" ); |
| 2739 | assert((!LI || !StoredValue) && "Stored value provided for widened load" ); |
| 2740 | |
| 2741 | LoopVectorizationCostModel::InstWidening Decision = |
| 2742 | Cost->getWideningDecision(Instr, VF); |
| 2743 | assert((Decision == LoopVectorizationCostModel::CM_Widen || |
| 2744 | Decision == LoopVectorizationCostModel::CM_Widen_Reverse || |
| 2745 | Decision == LoopVectorizationCostModel::CM_GatherScatter) && |
| 2746 | "CM decision is not to widen the memory instruction" ); |
| 2747 | |
| 2748 | Type *ScalarDataTy = getMemInstValueType(Instr); |
| 2749 | |
| 2750 | auto *DataTy = VectorType::get(ScalarDataTy, VF); |
| 2751 | const Align Alignment = getLoadStoreAlignment(Instr); |
| 2752 | |
| 2753 | // Determine if the pointer operand of the access is either consecutive or |
| 2754 | // reverse consecutive. |
| 2755 | bool Reverse = (Decision == LoopVectorizationCostModel::CM_Widen_Reverse); |
| 2756 | bool ConsecutiveStride = |
| 2757 | Reverse || (Decision == LoopVectorizationCostModel::CM_Widen); |
| 2758 | bool CreateGatherScatter = |
| 2759 | (Decision == LoopVectorizationCostModel::CM_GatherScatter); |
| 2760 | |
| 2761 | // Either Ptr feeds a vector load/store, or a vector GEP should feed a vector |
| 2762 | // gather/scatter. Otherwise Decision should have been to Scalarize. |
| 2763 | assert((ConsecutiveStride || CreateGatherScatter) && |
| 2764 | "The instruction should be scalarized" ); |
| 2765 | (void)ConsecutiveStride; |
| 2766 | |
| 2767 | VectorParts BlockInMaskParts(UF); |
| 2768 | bool isMaskRequired = BlockInMask; |
| 2769 | if (isMaskRequired) |
| 2770 | for (unsigned Part = 0; Part < UF; ++Part) |
| 2771 | BlockInMaskParts[Part] = State.get(BlockInMask, Part); |
| 2772 | |
| 2773 | const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * { |
| 2774 | // Calculate the pointer for the specific unroll-part. |
| 2775 | GetElementPtrInst *PartPtr = nullptr; |
| 2776 | |
| 2777 | bool InBounds = false; |
| 2778 | if (auto *gep = dyn_cast<GetElementPtrInst>(Ptr->stripPointerCasts())) |
| 2779 | InBounds = gep->isInBounds(); |
| 2780 | |
| 2781 | if (Reverse) { |
| 2782 | assert(!VF.isScalable() && |
| 2783 | "Reversing vectors is not yet supported for scalable vectors." ); |
| 2784 | |
| 2785 | // If the address is consecutive but reversed, then the |
| 2786 | // wide store needs to start at the last vector element. |
| 2787 | PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP( |
| 2788 | ScalarDataTy, Ptr, Builder.getInt32(-Part * VF.getKnownMinValue()))); |
| 2789 | PartPtr->setIsInBounds(InBounds); |
| 2790 | PartPtr = cast<GetElementPtrInst>(Builder.CreateGEP( |
| 2791 | ScalarDataTy, PartPtr, Builder.getInt32(1 - VF.getKnownMinValue()))); |
| 2792 | PartPtr->setIsInBounds(InBounds); |
| 2793 | if (isMaskRequired) // Reverse of a null all-one mask is a null mask. |
| 2794 | BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]); |
| 2795 | } else { |
| 2796 | Value *Increment = createStepForVF(Builder, Builder.getInt32(Part), VF); |
| 2797 | PartPtr = cast<GetElementPtrInst>( |
| 2798 | Builder.CreateGEP(ScalarDataTy, Ptr, Increment)); |
| 2799 | PartPtr->setIsInBounds(InBounds); |
| 2800 | } |
| 2801 | |
| 2802 | unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace(); |
| 2803 | return Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace)); |
| 2804 | }; |
| 2805 | |
| 2806 | // Handle Stores: |
| 2807 | if (SI) { |
| 2808 | setDebugLocFromInst(Builder, SI); |
| 2809 | |
| 2810 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2811 | Instruction *NewSI = nullptr; |
| 2812 | Value *StoredVal = State.get(StoredValue, Part); |
| 2813 | if (CreateGatherScatter) { |
| 2814 | Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| 2815 | Value *VectorGep = State.get(Addr, Part); |
| 2816 | NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment, |
| 2817 | MaskPart); |
| 2818 | } else { |
| 2819 | if (Reverse) { |
| 2820 | // If we store to reverse consecutive memory locations, then we need |
| 2821 | // to reverse the order of elements in the stored value. |
| 2822 | StoredVal = reverseVector(StoredVal); |
| 2823 | // We don't want to update the value in the map as it might be used in |
| 2824 | // another expression. So don't call resetVectorValue(StoredVal). |
| 2825 | } |
| 2826 | auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0})); |
| 2827 | if (isMaskRequired) |
| 2828 | NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment, |
| 2829 | BlockInMaskParts[Part]); |
| 2830 | else |
| 2831 | NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment); |
| 2832 | } |
| 2833 | addMetadata(NewSI, SI); |
| 2834 | } |
| 2835 | return; |
| 2836 | } |
| 2837 | |
| 2838 | // Handle loads. |
| 2839 | assert(LI && "Must have a load instruction" ); |
| 2840 | setDebugLocFromInst(Builder, LI); |
| 2841 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2842 | Value *NewLI; |
| 2843 | if (CreateGatherScatter) { |
| 2844 | Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| 2845 | Value *VectorGep = State.get(Addr, Part); |
| 2846 | NewLI = Builder.CreateMaskedGather(VectorGep, Alignment, MaskPart, |
| 2847 | nullptr, "wide.masked.gather" ); |
| 2848 | addMetadata(NewLI, LI); |
| 2849 | } else { |
| 2850 | auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0})); |
| 2851 | if (isMaskRequired) |
| 2852 | NewLI = Builder.CreateMaskedLoad( |
| 2853 | VecPtr, Alignment, BlockInMaskParts[Part], PoisonValue::get(DataTy), |
| 2854 | "wide.masked.load" ); |
| 2855 | else |
| 2856 | NewLI = |
| 2857 | Builder.CreateAlignedLoad(DataTy, VecPtr, Alignment, "wide.load" ); |
| 2858 | |
| 2859 | // Add metadata to the load, but setVectorValue to the reverse shuffle. |
| 2860 | addMetadata(NewLI, LI); |
| 2861 | if (Reverse) |
| 2862 | NewLI = reverseVector(NewLI); |
| 2863 | } |
| 2864 | |
| 2865 | State.set(Def, Instr, NewLI, Part); |
| 2866 | } |
| 2867 | } |
| 2868 | |
| 2869 | void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, VPUser &User, |
| 2870 | const VPIteration &Instance, |
| 2871 | bool IfPredicateInstr, |
| 2872 | VPTransformState &State) { |
| 2873 | assert(!Instr->getType()->isAggregateType() && "Can't handle vectors" ); |
| 2874 | |
| 2875 | // llvm.experimental.noalias.scope.decl intrinsics must only be duplicated for |
| 2876 | // the first lane and part. |
| 2877 | if (isa<NoAliasScopeDeclInst>(Instr)) |
| 2878 | if (Instance.Lane != 0 || Instance.Part != 0) |
| 2879 | return; |
| 2880 | |
| 2881 | setDebugLocFromInst(Builder, Instr); |
| 2882 | |
| 2883 | // Does this instruction return a value ? |
| 2884 | bool IsVoidRetTy = Instr->getType()->isVoidTy(); |
| 2885 | |
| 2886 | Instruction *Cloned = Instr->clone(); |
| 2887 | if (!IsVoidRetTy) |
| 2888 | Cloned->setName(Instr->getName() + ".cloned" ); |
| 2889 | |
| 2890 | // Replace the operands of the cloned instructions with their scalar |
| 2891 | // equivalents in the new loop. |
| 2892 | for (unsigned op = 0, e = User.getNumOperands(); op != e; ++op) { |
| 2893 | auto *Operand = dyn_cast<Instruction>(Instr->getOperand(op)); |
| 2894 | auto InputInstance = Instance; |
| 2895 | if (!Operand || !OrigLoop->contains(Operand) || |
| 2896 | (Cost->isUniformAfterVectorization(Operand, State.VF))) |
| 2897 | InputInstance.Lane = 0; |
| 2898 | auto *NewOp = State.get(User.getOperand(op), InputInstance); |
| 2899 | Cloned->setOperand(op, NewOp); |
| 2900 | } |
| 2901 | addNewMetadata(Cloned, Instr); |
| 2902 | |
| 2903 | // Place the cloned scalar in the new loop. |
| 2904 | Builder.Insert(Cloned); |
| 2905 | |
| 2906 | // TODO: Set result for VPValue of VPReciplicateRecipe. This requires |
| 2907 | // representing scalar values in VPTransformState. Add the cloned scalar to |
| 2908 | // the scalar map entry. |
| 2909 | VectorLoopValueMap.setScalarValue(Instr, Instance, Cloned); |
| 2910 | |
| 2911 | // If we just cloned a new assumption, add it the assumption cache. |
| 2912 | if (auto *II = dyn_cast<IntrinsicInst>(Cloned)) |
| 2913 | if (II->getIntrinsicID() == Intrinsic::assume) |
| 2914 | AC->registerAssumption(II); |
| 2915 | |
| 2916 | // End if-block. |
| 2917 | if (IfPredicateInstr) |
| 2918 | PredicatedInstructions.push_back(Cloned); |
| 2919 | } |
| 2920 | |
| 2921 | PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start, |
| 2922 | Value *End, Value *Step, |
| 2923 | Instruction *DL) { |
| 2924 | BasicBlock * = L->getHeader(); |
| 2925 | BasicBlock *Latch = L->getLoopLatch(); |
| 2926 | // As we're just creating this loop, it's possible no latch exists |
| 2927 | // yet. If so, use the header as this will be a single block loop. |
| 2928 | if (!Latch) |
| 2929 | Latch = Header; |
| 2930 | |
| 2931 | IRBuilder<> Builder(&*Header->getFirstInsertionPt()); |
| 2932 | Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction); |
| 2933 | setDebugLocFromInst(Builder, OldInst); |
| 2934 | auto *Induction = Builder.CreatePHI(Start->getType(), 2, "index" ); |
| 2935 | |
| 2936 | Builder.SetInsertPoint(Latch->getTerminator()); |
| 2937 | setDebugLocFromInst(Builder, OldInst); |
| 2938 | |
| 2939 | // Create i+1 and fill the PHINode. |
| 2940 | Value *Next = Builder.CreateAdd(Induction, Step, "index.next" ); |
| 2941 | Induction->addIncoming(Start, L->getLoopPreheader()); |
| 2942 | Induction->addIncoming(Next, Latch); |
| 2943 | // Create the compare. |
| 2944 | Value *ICmp = Builder.CreateICmpEQ(Next, End); |
| 2945 | Builder.CreateCondBr(ICmp, L->getUniqueExitBlock(), Header); |
| 2946 | |
| 2947 | // Now we have two terminators. Remove the old one from the block. |
| 2948 | Latch->getTerminator()->eraseFromParent(); |
| 2949 | |
| 2950 | return Induction; |
| 2951 | } |
| 2952 | |
| 2953 | Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) { |
| 2954 | if (TripCount) |
| 2955 | return TripCount; |
| 2956 | |
| 2957 | assert(L && "Create Trip Count for null loop." ); |
| 2958 | IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| 2959 | // Find the loop boundaries. |
| 2960 | ScalarEvolution *SE = PSE.getSE(); |
| 2961 | const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); |
| 2962 | assert(!isa<SCEVCouldNotCompute>(BackedgeTakenCount) && |
| 2963 | "Invalid loop count" ); |
| 2964 | |
| 2965 | Type *IdxTy = Legal->getWidestInductionType(); |
| 2966 | assert(IdxTy && "No type for induction" ); |
| 2967 | |
| 2968 | // The exit count might have the type of i64 while the phi is i32. This can |
| 2969 | // happen if we have an induction variable that is sign extended before the |
| 2970 | // compare. The only way that we get a backedge taken count is that the |
| 2971 | // induction variable was signed and as such will not overflow. In such a case |
| 2972 | // truncation is legal. |
| 2973 | if (SE->getTypeSizeInBits(BackedgeTakenCount->getType()) > |
| 2974 | IdxTy->getPrimitiveSizeInBits()) |
| 2975 | BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy); |
| 2976 | BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy); |
| 2977 | |
| 2978 | // Get the total trip count from the count by adding 1. |
| 2979 | const SCEV *ExitCount = SE->getAddExpr( |
| 2980 | BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); |
| 2981 | |
| 2982 | const DataLayout &DL = L->getHeader()->getModule()->getDataLayout(); |
| 2983 | |
| 2984 | // Expand the trip count and place the new instructions in the preheader. |
| 2985 | // Notice that the pre-header does not change, only the loop body. |
| 2986 | SCEVExpander Exp(*SE, DL, "induction" ); |
| 2987 | |
| 2988 | // Count holds the overall loop count (N). |
| 2989 | TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(), |
| 2990 | L->getLoopPreheader()->getTerminator()); |
| 2991 | |
| 2992 | if (TripCount->getType()->isPointerTy()) |
| 2993 | TripCount = |
| 2994 | CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int" , |
| 2995 | L->getLoopPreheader()->getTerminator()); |
| 2996 | |
| 2997 | return TripCount; |
| 2998 | } |
| 2999 | |
| 3000 | Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) { |
| 3001 | if (VectorTripCount) |
| 3002 | return VectorTripCount; |
| 3003 | |
| 3004 | Value *TC = getOrCreateTripCount(L); |
| 3005 | IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| 3006 | |
| 3007 | Type *Ty = TC->getType(); |
| 3008 | // This is where we can make the step a runtime constant. |
| 3009 | Value *Step = createStepForVF(Builder, ConstantInt::get(Ty, UF), VF); |
| 3010 | |
| 3011 | // If the tail is to be folded by masking, round the number of iterations N |
| 3012 | // up to a multiple of Step instead of rounding down. This is done by first |
| 3013 | // adding Step-1 and then rounding down. Note that it's ok if this addition |
| 3014 | // overflows: the vector induction variable will eventually wrap to zero given |
| 3015 | // that it starts at zero and its Step is a power of two; the loop will then |
| 3016 | // exit, with the last early-exit vector comparison also producing all-true. |
| 3017 | if (Cost->foldTailByMasking()) { |
| 3018 | assert(isPowerOf2_32(VF.getKnownMinValue() * UF) && |
| 3019 | "VF*UF must be a power of 2 when folding tail by masking" ); |
| 3020 | assert(!VF.isScalable() && |
| 3021 | "Tail folding not yet supported for scalable vectors" ); |
| 3022 | TC = Builder.CreateAdd( |
| 3023 | TC, ConstantInt::get(Ty, VF.getKnownMinValue() * UF - 1), "n.rnd.up" ); |
| 3024 | } |
| 3025 | |
| 3026 | // Now we need to generate the expression for the part of the loop that the |
| 3027 | // vectorized body will execute. This is equal to N - (N % Step) if scalar |
| 3028 | // iterations are not required for correctness, or N - Step, otherwise. Step |
| 3029 | // is equal to the vectorization factor (number of SIMD elements) times the |
| 3030 | // unroll factor (number of SIMD instructions). |
| 3031 | Value *R = Builder.CreateURem(TC, Step, "n.mod.vf" ); |
| 3032 | |
| 3033 | // There are two cases where we need to ensure (at least) the last iteration |
| 3034 | // runs in the scalar remainder loop. Thus, if the step evenly divides |
| 3035 | // the trip count, we set the remainder to be equal to the step. If the step |
| 3036 | // does not evenly divide the trip count, no adjustment is necessary since |
| 3037 | // there will already be scalar iterations. Note that the minimum iterations |
| 3038 | // check ensures that N >= Step. The cases are: |
| 3039 | // 1) If there is a non-reversed interleaved group that may speculatively |
| 3040 | // access memory out-of-bounds. |
| 3041 | // 2) If any instruction may follow a conditionally taken exit. That is, if |
| 3042 | // the loop contains multiple exiting blocks, or a single exiting block |
| 3043 | // which is not the latch. |
| 3044 | if (VF.isVector() && Cost->requiresScalarEpilogue()) { |
| 3045 | auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0)); |
| 3046 | R = Builder.CreateSelect(IsZero, Step, R); |
| 3047 | } |
| 3048 | |
| 3049 | VectorTripCount = Builder.CreateSub(TC, R, "n.vec" ); |
| 3050 | |
| 3051 | return VectorTripCount; |
| 3052 | } |
| 3053 | |
| 3054 | Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| 3055 | const DataLayout &DL) { |
| 3056 | // Verify that V is a vector type with same number of elements as DstVTy. |
| 3057 | auto *DstFVTy = cast<FixedVectorType>(DstVTy); |
| 3058 | unsigned VF = DstFVTy->getNumElements(); |
| 3059 | auto *SrcVecTy = cast<FixedVectorType>(V->getType()); |
| 3060 | assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match" ); |
| 3061 | Type *SrcElemTy = SrcVecTy->getElementType(); |
| 3062 | Type *DstElemTy = DstFVTy->getElementType(); |
| 3063 | assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) && |
| 3064 | "Vector elements must have same size" ); |
| 3065 | |
| 3066 | // Do a direct cast if element types are castable. |
| 3067 | if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) { |
| 3068 | return Builder.CreateBitOrPointerCast(V, DstFVTy); |
| 3069 | } |
| 3070 | // V cannot be directly casted to desired vector type. |
| 3071 | // May happen when V is a floating point vector but DstVTy is a vector of |
| 3072 | // pointers or vice-versa. Handle this using a two-step bitcast using an |
| 3073 | // intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float. |
| 3074 | assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) && |
| 3075 | "Only one type should be a pointer type" ); |
| 3076 | assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) && |
| 3077 | "Only one type should be a floating point type" ); |
| 3078 | Type *IntTy = |
| 3079 | IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy)); |
| 3080 | auto *VecIntTy = FixedVectorType::get(IntTy, VF); |
| 3081 | Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy); |
| 3082 | return Builder.CreateBitOrPointerCast(CastVal, DstFVTy); |
| 3083 | } |
| 3084 | |
| 3085 | void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L, |
| 3086 | BasicBlock *Bypass) { |
| 3087 | Value *Count = getOrCreateTripCount(L); |
| 3088 | // Reuse existing vector loop preheader for TC checks. |
| 3089 | // Note that new preheader block is generated for vector loop. |
| 3090 | BasicBlock *const TCCheckBlock = LoopVectorPreHeader; |
| 3091 | IRBuilder<> Builder(TCCheckBlock->getTerminator()); |
| 3092 | |
| 3093 | // Generate code to check if the loop's trip count is less than VF * UF, or |
| 3094 | // equal to it in case a scalar epilogue is required; this implies that the |
| 3095 | // vector trip count is zero. This check also covers the case where adding one |
| 3096 | // to the backedge-taken count overflowed leading to an incorrect trip count |
| 3097 | // of zero. In this case we will also jump to the scalar loop. |
| 3098 | auto P = Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE |
| 3099 | : ICmpInst::ICMP_ULT; |
| 3100 | |
| 3101 | // If tail is to be folded, vector loop takes care of all iterations. |
| 3102 | Value *CheckMinIters = Builder.getFalse(); |
| 3103 | if (!Cost->foldTailByMasking()) { |
| 3104 | Value *Step = |
| 3105 | createStepForVF(Builder, ConstantInt::get(Count->getType(), UF), VF); |
| 3106 | CheckMinIters = Builder.CreateICmp(P, Count, Step, "min.iters.check" ); |
| 3107 | } |
| 3108 | // Create new preheader for vector loop. |
| 3109 | LoopVectorPreHeader = |
| 3110 | SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr, |
| 3111 | "vector.ph" ); |
| 3112 | |
| 3113 | assert(DT->properlyDominates(DT->getNode(TCCheckBlock), |
| 3114 | DT->getNode(Bypass)->getIDom()) && |
| 3115 | "TC check is expected to dominate Bypass" ); |
| 3116 | |
| 3117 | // Update dominator for Bypass & LoopExit. |
| 3118 | DT->changeImmediateDominator(Bypass, TCCheckBlock); |
| 3119 | DT->changeImmediateDominator(LoopExitBlock, TCCheckBlock); |
| 3120 | |
| 3121 | ReplaceInstWithInst( |
| 3122 | TCCheckBlock->getTerminator(), |
| 3123 | BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| 3124 | LoopBypassBlocks.push_back(TCCheckBlock); |
| 3125 | } |
| 3126 | |
| 3127 | void InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) { |
| 3128 | // Reuse existing vector loop preheader for SCEV checks. |
| 3129 | // Note that new preheader block is generated for vector loop. |
| 3130 | BasicBlock *const SCEVCheckBlock = LoopVectorPreHeader; |
| 3131 | |
| 3132 | // Generate the code to check that the SCEV assumptions that we made. |
| 3133 | // We want the new basic block to start at the first instruction in a |
| 3134 | // sequence of instructions that form a check. |
| 3135 | SCEVExpander Exp(*PSE.getSE(), Bypass->getModule()->getDataLayout(), |
| 3136 | "scev.check" ); |
| 3137 | Value *SCEVCheck = Exp.expandCodeForPredicate( |
| 3138 | &PSE.getUnionPredicate(), SCEVCheckBlock->getTerminator()); |
| 3139 | |
| 3140 | if (auto *C = dyn_cast<ConstantInt>(SCEVCheck)) |
| 3141 | if (C->isZero()) |
| 3142 | return; |
| 3143 | |
| 3144 | assert(!(SCEVCheckBlock->getParent()->hasOptSize() || |
| 3145 | (OptForSizeBasedOnProfile && |
| 3146 | Cost->Hints->getForce() != LoopVectorizeHints::FK_Enabled)) && |
| 3147 | "Cannot SCEV check stride or overflow when optimizing for size" ); |
| 3148 | |
| 3149 | SCEVCheckBlock->setName("vector.scevcheck" ); |
| 3150 | // Create new preheader for vector loop. |
| 3151 | LoopVectorPreHeader = |
| 3152 | SplitBlock(SCEVCheckBlock, SCEVCheckBlock->getTerminator(), DT, LI, |
| 3153 | nullptr, "vector.ph" ); |
| 3154 | |
| 3155 | // Update dominator only if this is first RT check. |
| 3156 | if (LoopBypassBlocks.empty()) { |
| 3157 | DT->changeImmediateDominator(Bypass, SCEVCheckBlock); |
| 3158 | DT->changeImmediateDominator(LoopExitBlock, SCEVCheckBlock); |
| 3159 | } |
| 3160 | |
| 3161 | ReplaceInstWithInst( |
| 3162 | SCEVCheckBlock->getTerminator(), |
| 3163 | BranchInst::Create(Bypass, LoopVectorPreHeader, SCEVCheck)); |
| 3164 | LoopBypassBlocks.push_back(SCEVCheckBlock); |
| 3165 | AddedSafetyChecks = true; |
| 3166 | } |
| 3167 | |
| 3168 | void InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass) { |
| 3169 | // VPlan-native path does not do any analysis for runtime checks currently. |
| 3170 | if (EnableVPlanNativePath) |
| 3171 | return; |
| 3172 | |
| 3173 | // Reuse existing vector loop preheader for runtime memory checks. |
| 3174 | // Note that new preheader block is generated for vector loop. |
| 3175 | BasicBlock *const MemCheckBlock = L->getLoopPreheader(); |
| 3176 | |
| 3177 | // Generate the code that checks in runtime if arrays overlap. We put the |
| 3178 | // checks into a separate block to make the more common case of few elements |
| 3179 | // faster. |
| 3180 | auto *LAI = Legal->getLAI(); |
| 3181 | const auto &RtPtrChecking = *LAI->getRuntimePointerChecking(); |
| 3182 | if (!RtPtrChecking.Need) |
| 3183 | return; |
| 3184 | |
| 3185 | if (MemCheckBlock->getParent()->hasOptSize() || OptForSizeBasedOnProfile) { |
| 3186 | assert(Cost->Hints->getForce() == LoopVectorizeHints::FK_Enabled && |
| 3187 | "Cannot emit memory checks when optimizing for size, unless forced " |
| 3188 | "to vectorize." ); |
| 3189 | ORE->emit([&]() { |
| 3190 | return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationCodeSize" , |
| 3191 | L->getStartLoc(), L->getHeader()) |
| 3192 | << "Code-size may be reduced by not forcing " |
| 3193 | "vectorization, or by source-code modifications " |
| 3194 | "eliminating the need for runtime checks " |
| 3195 | "(e.g., adding 'restrict')." ; |
| 3196 | }); |
| 3197 | } |
| 3198 | |
| 3199 | MemCheckBlock->setName("vector.memcheck" ); |
| 3200 | // Create new preheader for vector loop. |
| 3201 | LoopVectorPreHeader = |
| 3202 | SplitBlock(MemCheckBlock, MemCheckBlock->getTerminator(), DT, LI, nullptr, |
| 3203 | "vector.ph" ); |
| 3204 | |
| 3205 | auto *CondBranch = cast<BranchInst>( |
| 3206 | Builder.CreateCondBr(Builder.getTrue(), Bypass, LoopVectorPreHeader)); |
| 3207 | ReplaceInstWithInst(MemCheckBlock->getTerminator(), CondBranch); |
| 3208 | LoopBypassBlocks.push_back(MemCheckBlock); |
| 3209 | AddedSafetyChecks = true; |
| 3210 | |
| 3211 | // Update dominator only if this is first RT check. |
| 3212 | if (LoopBypassBlocks.empty()) { |
| 3213 | DT->changeImmediateDominator(Bypass, MemCheckBlock); |
| 3214 | DT->changeImmediateDominator(LoopExitBlock, MemCheckBlock); |
| 3215 | } |
| 3216 | |
| 3217 | Instruction *FirstCheckInst; |
| 3218 | Instruction *MemRuntimeCheck; |
| 3219 | std::tie(FirstCheckInst, MemRuntimeCheck) = |
| 3220 | addRuntimeChecks(MemCheckBlock->getTerminator(), OrigLoop, |
| 3221 | RtPtrChecking.getChecks(), RtPtrChecking.getSE()); |
| 3222 | assert(MemRuntimeCheck && "no RT checks generated although RtPtrChecking " |
| 3223 | "claimed checks are required" ); |
| 3224 | CondBranch->setCondition(MemRuntimeCheck); |
| 3225 | |
| 3226 | // We currently don't use LoopVersioning for the actual loop cloning but we |
| 3227 | // still use it to add the noalias metadata. |
| 3228 | LVer = std::make_unique<LoopVersioning>( |
| 3229 | *Legal->getLAI(), |
| 3230 | Legal->getLAI()->getRuntimePointerChecking()->getChecks(), OrigLoop, LI, |
| 3231 | DT, PSE.getSE()); |
| 3232 | LVer->prepareNoAliasMetadata(); |
| 3233 | } |
| 3234 | |
| 3235 | Value *InnerLoopVectorizer::emitTransformedIndex( |
| 3236 | IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL, |
| 3237 | const InductionDescriptor &ID) const { |
| 3238 | |
| 3239 | SCEVExpander Exp(*SE, DL, "induction" ); |
| 3240 | auto Step = ID.getStep(); |
| 3241 | auto StartValue = ID.getStartValue(); |
| 3242 | assert(Index->getType() == Step->getType() && |
| 3243 | "Index type does not match StepValue type" ); |
| 3244 | |
| 3245 | // Note: the IR at this point is broken. We cannot use SE to create any new |
| 3246 | // SCEV and then expand it, hoping that SCEV's simplification will give us |
| 3247 | // a more optimal code. Unfortunately, attempt of doing so on invalid IR may |
| 3248 | // lead to various SCEV crashes. So all we can do is to use builder and rely |
| 3249 | // on InstCombine for future simplifications. Here we handle some trivial |
| 3250 | // cases only. |
| 3251 | auto CreateAdd = [&B](Value *X, Value *Y) { |
| 3252 | assert(X->getType() == Y->getType() && "Types don't match!" ); |
| 3253 | if (auto *CX = dyn_cast<ConstantInt>(X)) |
| 3254 | if (CX->isZero()) |
| 3255 | return Y; |
| 3256 | if (auto *CY = dyn_cast<ConstantInt>(Y)) |
| 3257 | if (CY->isZero()) |
| 3258 | return X; |
| 3259 | return B.CreateAdd(X, Y); |
| 3260 | }; |
| 3261 | |
| 3262 | auto CreateMul = [&B](Value *X, Value *Y) { |
| 3263 | assert(X->getType() == Y->getType() && "Types don't match!" ); |
| 3264 | if (auto *CX = dyn_cast<ConstantInt>(X)) |
| 3265 | if (CX->isOne()) |
| 3266 | return Y; |
| 3267 | if (auto *CY = dyn_cast<ConstantInt>(Y)) |
| 3268 | if (CY->isOne()) |
| 3269 | return X; |
| 3270 | return B.CreateMul(X, Y); |
| 3271 | }; |
| 3272 | |
| 3273 | // Get a suitable insert point for SCEV expansion. For blocks in the vector |
| 3274 | // loop, choose the end of the vector loop header (=LoopVectorBody), because |
| 3275 | // the DomTree is not kept up-to-date for additional blocks generated in the |
| 3276 | // vector loop. By using the header as insertion point, we guarantee that the |
| 3277 | // expanded instructions dominate all their uses. |
| 3278 | auto GetInsertPoint = [this, &B]() { |
| 3279 | BasicBlock *InsertBB = B.GetInsertPoint()->getParent(); |
| 3280 | if (InsertBB != LoopVectorBody && |
| 3281 | LI->getLoopFor(LoopVectorBody) == LI->getLoopFor(InsertBB)) |
| 3282 | return LoopVectorBody->getTerminator(); |
| 3283 | return &*B.GetInsertPoint(); |
| 3284 | }; |
| 3285 | switch (ID.getKind()) { |
| 3286 | case InductionDescriptor::IK_IntInduction: { |
| 3287 | assert(Index->getType() == StartValue->getType() && |
| 3288 | "Index type does not match StartValue type" ); |
| 3289 | if (ID.getConstIntStepValue() && ID.getConstIntStepValue()->isMinusOne()) |
| 3290 | return B.CreateSub(StartValue, Index); |
| 3291 | auto *Offset = CreateMul( |
| 3292 | Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint())); |
| 3293 | return CreateAdd(StartValue, Offset); |
| 3294 | } |
| 3295 | case InductionDescriptor::IK_PtrInduction: { |
| 3296 | assert(isa<SCEVConstant>(Step) && |
| 3297 | "Expected constant step for pointer induction" ); |
| 3298 | return B.CreateGEP( |
| 3299 | StartValue->getType()->getPointerElementType(), StartValue, |
| 3300 | CreateMul(Index, |
| 3301 | Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint()))); |
| 3302 | } |
| 3303 | case InductionDescriptor::IK_FpInduction: { |
| 3304 | assert(Step->getType()->isFloatingPointTy() && "Expected FP Step value" ); |
| 3305 | auto InductionBinOp = ID.getInductionBinOp(); |
| 3306 | assert(InductionBinOp && |
| 3307 | (InductionBinOp->getOpcode() == Instruction::FAdd || |
| 3308 | InductionBinOp->getOpcode() == Instruction::FSub) && |
| 3309 | "Original bin op should be defined for FP induction" ); |
| 3310 | |
| 3311 | Value *StepValue = cast<SCEVUnknown>(Step)->getValue(); |
| 3312 | |
| 3313 | // Floating point operations had to be 'fast' to enable the induction. |
| 3314 | FastMathFlags Flags; |
| 3315 | Flags.setFast(); |
| 3316 | |
| 3317 | Value *MulExp = B.CreateFMul(StepValue, Index); |
| 3318 | if (isa<Instruction>(MulExp)) |
| 3319 | // We have to check, the MulExp may be a constant. |
| 3320 | cast<Instruction>(MulExp)->setFastMathFlags(Flags); |
| 3321 | |
| 3322 | Value *BOp = B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp, |
| 3323 | "induction" ); |
| 3324 | if (isa<Instruction>(BOp)) |
| 3325 | cast<Instruction>(BOp)->setFastMathFlags(Flags); |
| 3326 | |
| 3327 | return BOp; |
| 3328 | } |
| 3329 | case InductionDescriptor::IK_NoInduction: |
| 3330 | return nullptr; |
| 3331 | } |
| 3332 | llvm_unreachable("invalid enum" ); |
| 3333 | } |
| 3334 | |
| 3335 | Loop *InnerLoopVectorizer::createVectorLoopSkeleton(StringRef Prefix) { |
| 3336 | LoopScalarBody = OrigLoop->getHeader(); |
| 3337 | LoopVectorPreHeader = OrigLoop->getLoopPreheader(); |
| 3338 | LoopExitBlock = OrigLoop->getUniqueExitBlock(); |
| 3339 | assert(LoopExitBlock && "Must have an exit block" ); |
| 3340 | assert(LoopVectorPreHeader && "Invalid loop structure" ); |
| 3341 | |
| 3342 | LoopMiddleBlock = |
| 3343 | SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| 3344 | LI, nullptr, Twine(Prefix) + "middle.block" ); |
| 3345 | LoopScalarPreHeader = |
| 3346 | SplitBlock(LoopMiddleBlock, LoopMiddleBlock->getTerminator(), DT, LI, |
| 3347 | nullptr, Twine(Prefix) + "scalar.ph" ); |
| 3348 | |
| 3349 | // Set up branch from middle block to the exit and scalar preheader blocks. |
| 3350 | // completeLoopSkeleton will update the condition to use an iteration check, |
| 3351 | // if required to decide whether to execute the remainder. |
| 3352 | BranchInst *BrInst = |
| 3353 | BranchInst::Create(LoopExitBlock, LoopScalarPreHeader, Builder.getTrue()); |
| 3354 | auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| 3355 | BrInst->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| 3356 | ReplaceInstWithInst(LoopMiddleBlock->getTerminator(), BrInst); |
| 3357 | |
| 3358 | // We intentionally don't let SplitBlock to update LoopInfo since |
| 3359 | // LoopVectorBody should belong to another loop than LoopVectorPreHeader. |
| 3360 | // LoopVectorBody is explicitly added to the correct place few lines later. |
| 3361 | LoopVectorBody = |
| 3362 | SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| 3363 | nullptr, nullptr, Twine(Prefix) + "vector.body" ); |
| 3364 | |
| 3365 | // Update dominator for loop exit. |
| 3366 | DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock); |
| 3367 | |
| 3368 | // Create and register the new vector loop. |
| 3369 | Loop *Lp = LI->AllocateLoop(); |
| 3370 | Loop *ParentLoop = OrigLoop->getParentLoop(); |
| 3371 | |
| 3372 | // Insert the new loop into the loop nest and register the new basic blocks |
| 3373 | // before calling any utilities such as SCEV that require valid LoopInfo. |
| 3374 | if (ParentLoop) { |
| 3375 | ParentLoop->addChildLoop(Lp); |
| 3376 | } else { |
| 3377 | LI->addTopLevelLoop(Lp); |
| 3378 | } |
| 3379 | Lp->addBasicBlockToLoop(LoopVectorBody, *LI); |
| 3380 | return Lp; |
| 3381 | } |
| 3382 | |
| 3383 | void InnerLoopVectorizer::createInductionResumeValues( |
| 3384 | Loop *L, Value *VectorTripCount, |
| 3385 | std::pair<BasicBlock *, Value *> AdditionalBypass) { |
| 3386 | assert(VectorTripCount && L && "Expected valid arguments" ); |
| 3387 | assert(((AdditionalBypass.first && AdditionalBypass.second) || |
| 3388 | (!AdditionalBypass.first && !AdditionalBypass.second)) && |
| 3389 | "Inconsistent information about additional bypass." ); |
| 3390 | // We are going to resume the execution of the scalar loop. |
| 3391 | // Go over all of the induction variables that we found and fix the |
| 3392 | // PHIs that are left in the scalar version of the loop. |
| 3393 | // The starting values of PHI nodes depend on the counter of the last |
| 3394 | // iteration in the vectorized loop. |
| 3395 | // If we come from a bypass edge then we need to start from the original |
| 3396 | // start value. |
| 3397 | for (auto &InductionEntry : Legal->getInductionVars()) { |
| 3398 | PHINode *OrigPhi = InductionEntry.first; |
| 3399 | InductionDescriptor II = InductionEntry.second; |
| 3400 | |
| 3401 | // Create phi nodes to merge from the backedge-taken check block. |
| 3402 | PHINode *BCResumeVal = |
| 3403 | PHINode::Create(OrigPhi->getType(), 3, "bc.resume.val" , |
| 3404 | LoopScalarPreHeader->getTerminator()); |
| 3405 | // Copy original phi DL over to the new one. |
| 3406 | BCResumeVal->setDebugLoc(OrigPhi->getDebugLoc()); |
| 3407 | Value *&EndValue = IVEndValues[OrigPhi]; |
| 3408 | Value *EndValueFromAdditionalBypass = AdditionalBypass.second; |
| 3409 | if (OrigPhi == OldInduction) { |
| 3410 | // We know what the end value is. |
| 3411 | EndValue = VectorTripCount; |
| 3412 | } else { |
| 3413 | IRBuilder<> B(L->getLoopPreheader()->getTerminator()); |
| 3414 | Type *StepType = II.getStep()->getType(); |
| 3415 | Instruction::CastOps CastOp = |
| 3416 | CastInst::getCastOpcode(VectorTripCount, true, StepType, true); |
| 3417 | Value *CRD = B.CreateCast(CastOp, VectorTripCount, StepType, "cast.crd" ); |
| 3418 | const DataLayout &DL = LoopScalarBody->getModule()->getDataLayout(); |
| 3419 | EndValue = emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| 3420 | EndValue->setName("ind.end" ); |
| 3421 | |
| 3422 | // Compute the end value for the additional bypass (if applicable). |
| 3423 | if (AdditionalBypass.first) { |
| 3424 | B.SetInsertPoint(&(*AdditionalBypass.first->getFirstInsertionPt())); |
| 3425 | CastOp = CastInst::getCastOpcode(AdditionalBypass.second, true, |
| 3426 | StepType, true); |
| 3427 | CRD = |
| 3428 | B.CreateCast(CastOp, AdditionalBypass.second, StepType, "cast.crd" ); |
| 3429 | EndValueFromAdditionalBypass = |
| 3430 | emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| 3431 | EndValueFromAdditionalBypass->setName("ind.end" ); |
| 3432 | } |
| 3433 | } |
| 3434 | // The new PHI merges the original incoming value, in case of a bypass, |
| 3435 | // or the value at the end of the vectorized loop. |
| 3436 | BCResumeVal->addIncoming(EndValue, LoopMiddleBlock); |
| 3437 | |
| 3438 | // Fix the scalar body counter (PHI node). |
| 3439 | // The old induction's phi node in the scalar body needs the truncated |
| 3440 | // value. |
| 3441 | for (BasicBlock *BB : LoopBypassBlocks) |
| 3442 | BCResumeVal->addIncoming(II.getStartValue(), BB); |
| 3443 | |
| 3444 | if (AdditionalBypass.first) |
| 3445 | BCResumeVal->setIncomingValueForBlock(AdditionalBypass.first, |
| 3446 | EndValueFromAdditionalBypass); |
| 3447 | |
| 3448 | OrigPhi->setIncomingValueForBlock(LoopScalarPreHeader, BCResumeVal); |
| 3449 | } |
| 3450 | } |
| 3451 | |
| 3452 | BasicBlock *InnerLoopVectorizer::completeLoopSkeleton(Loop *L, |
| 3453 | MDNode *OrigLoopID) { |
| 3454 | assert(L && "Expected valid loop." ); |
| 3455 | |
| 3456 | // The trip counts should be cached by now. |
| 3457 | Value *Count = getOrCreateTripCount(L); |
| 3458 | Value *VectorTripCount = getOrCreateVectorTripCount(L); |
| 3459 | |
| 3460 | auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| 3461 | |
| 3462 | // Add a check in the middle block to see if we have completed |
| 3463 | // all of the iterations in the first vector loop. |
| 3464 | // If (N - N%VF) == N, then we *don't* need to run the remainder. |
| 3465 | // If tail is to be folded, we know we don't need to run the remainder. |
| 3466 | if (!Cost->foldTailByMasking()) { |
| 3467 | Instruction *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, |
| 3468 | Count, VectorTripCount, "cmp.n" , |
| 3469 | LoopMiddleBlock->getTerminator()); |
| 3470 | |
| 3471 | // Here we use the same DebugLoc as the scalar loop latch terminator instead |
| 3472 | // of the corresponding compare because they may have ended up with |
| 3473 | // different line numbers and we want to avoid awkward line stepping while |
| 3474 | // debugging. Eg. if the compare has got a line number inside the loop. |
| 3475 | CmpN->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| 3476 | cast<BranchInst>(LoopMiddleBlock->getTerminator())->setCondition(CmpN); |
| 3477 | } |
| 3478 | |
| 3479 | // Get ready to start creating new instructions into the vectorized body. |
| 3480 | assert(LoopVectorPreHeader == L->getLoopPreheader() && |
| 3481 | "Inconsistent vector loop preheader" ); |
| 3482 | Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt()); |
| 3483 | |
| 3484 | Optional<MDNode *> VectorizedLoopID = |
| 3485 | makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, |
| 3486 | LLVMLoopVectorizeFollowupVectorized}); |
| 3487 | if (VectorizedLoopID.hasValue()) { |
| 3488 | L->setLoopID(VectorizedLoopID.getValue()); |
| 3489 | |
| 3490 | // Do not setAlreadyVectorized if loop attributes have been defined |
| 3491 | // explicitly. |
| 3492 | return LoopVectorPreHeader; |
| 3493 | } |
| 3494 | |
| 3495 | // Keep all loop hints from the original loop on the vector loop (we'll |
| 3496 | // replace the vectorizer-specific hints below). |
| 3497 | if (MDNode *LID = OrigLoop->getLoopID()) |
| 3498 | L->setLoopID(LID); |
| 3499 | |
| 3500 | LoopVectorizeHints Hints(L, true, *ORE); |
| 3501 | Hints.setAlreadyVectorized(); |
| 3502 | |
| 3503 | #ifdef EXPENSIVE_CHECKS |
| 3504 | assert(DT->verify(DominatorTree::VerificationLevel::Fast)); |
| 3505 | LI->verify(*DT); |
| 3506 | #endif |
| 3507 | |
| 3508 | return LoopVectorPreHeader; |
| 3509 | } |
| 3510 | |
| 3511 | BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() { |
| 3512 | /* |
| 3513 | In this function we generate a new loop. The new loop will contain |
| 3514 | the vectorized instructions while the old loop will continue to run the |
| 3515 | scalar remainder. |
| 3516 | |
| 3517 | [ ] <-- loop iteration number check. |
| 3518 | / | |
| 3519 | / v |
| 3520 | | [ ] <-- vector loop bypass (may consist of multiple blocks). |
| 3521 | | / | |
| 3522 | | / v |
| 3523 | || [ ] <-- vector pre header. |
| 3524 | |/ | |
| 3525 | | v |
| 3526 | | [ ] \ |
| 3527 | | [ ]_| <-- vector loop. |
| 3528 | | | |
| 3529 | | v |
| 3530 | | -[ ] <--- middle-block. |
| 3531 | | / | |
| 3532 | | / v |
| 3533 | -|- >[ ] <--- new preheader. |
| 3534 | | | |
| 3535 | | v |
| 3536 | | [ ] \ |
| 3537 | | [ ]_| <-- old scalar loop to handle remainder. |
| 3538 | \ | |
| 3539 | \ v |
| 3540 | >[ ] <-- exit block. |
| 3541 | ... |
| 3542 | */ |
| 3543 | |
| 3544 | // Get the metadata of the original loop before it gets modified. |
| 3545 | MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| 3546 | |
| 3547 | // Create an empty vector loop, and prepare basic blocks for the runtime |
| 3548 | // checks. |
| 3549 | Loop *Lp = createVectorLoopSkeleton("" ); |
| 3550 | |
| 3551 | // Now, compare the new count to zero. If it is zero skip the vector loop and |
| 3552 | // jump to the scalar loop. This check also covers the case where the |
| 3553 | // backedge-taken count is uint##_max: adding one to it will overflow leading |
| 3554 | // to an incorrect trip count of zero. In this (rare) case we will also jump |
| 3555 | // to the scalar loop. |
| 3556 | emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader); |
| 3557 | |
| 3558 | // Generate the code to check any assumptions that we've made for SCEV |
| 3559 | // expressions. |
| 3560 | emitSCEVChecks(Lp, LoopScalarPreHeader); |
| 3561 | |
| 3562 | // Generate the code that checks in runtime if arrays overlap. We put the |
| 3563 | // checks into a separate block to make the more common case of few elements |
| 3564 | // faster. |
| 3565 | emitMemRuntimeChecks(Lp, LoopScalarPreHeader); |
| 3566 | |
| 3567 | // Some loops have a single integer induction variable, while other loops |
| 3568 | // don't. One example is c++ iterators that often have multiple pointer |
| 3569 | // induction variables. In the code below we also support a case where we |
| 3570 | // don't have a single induction variable. |
| 3571 | // |
| 3572 | // We try to obtain an induction variable from the original loop as hard |
| 3573 | // as possible. However if we don't find one that: |
| 3574 | // - is an integer |
| 3575 | // - counts from zero, stepping by one |
| 3576 | // - is the size of the widest induction variable type |
| 3577 | // then we create a new one. |
| 3578 | OldInduction = Legal->getPrimaryInduction(); |
| 3579 | Type *IdxTy = Legal->getWidestInductionType(); |
| 3580 | Value *StartIdx = ConstantInt::get(IdxTy, 0); |
| 3581 | // The loop step is equal to the vectorization factor (num of SIMD elements) |
| 3582 | // times the unroll factor (num of SIMD instructions). |
| 3583 | Builder.SetInsertPoint(&*Lp->getHeader()->getFirstInsertionPt()); |
| 3584 | Value *Step = createStepForVF(Builder, ConstantInt::get(IdxTy, UF), VF); |
| 3585 | Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| 3586 | Induction = |
| 3587 | createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| 3588 | getDebugLocFromInstOrOperands(OldInduction)); |
| 3589 | |
| 3590 | // Emit phis for the new starting index of the scalar loop. |
| 3591 | createInductionResumeValues(Lp, CountRoundDown); |
| 3592 | |
| 3593 | return completeLoopSkeleton(Lp, OrigLoopID); |
| 3594 | } |
| 3595 | |
| 3596 | // Fix up external users of the induction variable. At this point, we are |
| 3597 | // in LCSSA form, with all external PHIs that use the IV having one input value, |
| 3598 | // coming from the remainder loop. We need those PHIs to also have a correct |
| 3599 | // value for the IV when arriving directly from the middle block. |
| 3600 | void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi, |
| 3601 | const InductionDescriptor &II, |
| 3602 | Value *CountRoundDown, Value *EndValue, |
| 3603 | BasicBlock *MiddleBlock) { |
| 3604 | // There are two kinds of external IV usages - those that use the value |
| 3605 | // computed in the last iteration (the PHI) and those that use the penultimate |
| 3606 | // value (the value that feeds into the phi from the loop latch). |
| 3607 | // We allow both, but they, obviously, have different values. |
| 3608 | |
| 3609 | assert(OrigLoop->getUniqueExitBlock() && "Expected a single exit block" ); |
| 3610 | |
| 3611 | DenseMap<Value *, Value *> MissingVals; |
| 3612 | |
| 3613 | // An external user of the last iteration's value should see the value that |
| 3614 | // the remainder loop uses to initialize its own IV. |
| 3615 | Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch()); |
| 3616 | for (User *U : PostInc->users()) { |
| 3617 | Instruction *UI = cast<Instruction>(U); |
| 3618 | if (!OrigLoop->contains(UI)) { |
| 3619 | assert(isa<PHINode>(UI) && "Expected LCSSA form" ); |
| 3620 | MissingVals[UI] = EndValue; |
| 3621 | } |
| 3622 | } |
| 3623 | |
| 3624 | // An external user of the penultimate value need to see EndValue - Step. |
| 3625 | // The simplest way to get this is to recompute it from the constituent SCEVs, |
| 3626 | // that is Start + (Step * (CRD - 1)). |
| 3627 | for (User *U : OrigPhi->users()) { |
| 3628 | auto *UI = cast<Instruction>(U); |
| 3629 | if (!OrigLoop->contains(UI)) { |
| 3630 | const DataLayout &DL = |
| 3631 | OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 3632 | assert(isa<PHINode>(UI) && "Expected LCSSA form" ); |
| 3633 | |
| 3634 | IRBuilder<> B(MiddleBlock->getTerminator()); |
| 3635 | Value *CountMinusOne = B.CreateSub( |
| 3636 | CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1)); |
| 3637 | Value *CMO = |
| 3638 | !II.getStep()->getType()->isIntegerTy() |
| 3639 | ? B.CreateCast(Instruction::SIToFP, CountMinusOne, |
| 3640 | II.getStep()->getType()) |
| 3641 | : B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType()); |
| 3642 | CMO->setName("cast.cmo" ); |
| 3643 | Value *Escape = emitTransformedIndex(B, CMO, PSE.getSE(), DL, II); |
| 3644 | Escape->setName("ind.escape" ); |
| 3645 | MissingVals[UI] = Escape; |
| 3646 | } |
| 3647 | } |
| 3648 | |
| 3649 | for (auto &I : MissingVals) { |
| 3650 | PHINode *PHI = cast<PHINode>(I.first); |
| 3651 | // One corner case we have to handle is two IVs "chasing" each-other, |
| 3652 | // that is %IV2 = phi [...], [ %IV1, %latch ] |
| 3653 | // In this case, if IV1 has an external use, we need to avoid adding both |
| 3654 | // "last value of IV1" and "penultimate value of IV2". So, verify that we |
| 3655 | // don't already have an incoming value for the middle block. |
| 3656 | if (PHI->getBasicBlockIndex(MiddleBlock) == -1) |
| 3657 | PHI->addIncoming(I.second, MiddleBlock); |
| 3658 | } |
| 3659 | } |
| 3660 | |
| 3661 | namespace { |
| 3662 | |
| 3663 | struct CSEDenseMapInfo { |
| 3664 | static bool canHandle(const Instruction *I) { |
| 3665 | return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) || |
| 3666 | isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I); |
| 3667 | } |
| 3668 | |
| 3669 | static inline Instruction *getEmptyKey() { |
| 3670 | return DenseMapInfo<Instruction *>::getEmptyKey(); |
| 3671 | } |
| 3672 | |
| 3673 | static inline Instruction *getTombstoneKey() { |
| 3674 | return DenseMapInfo<Instruction *>::getTombstoneKey(); |
| 3675 | } |
| 3676 | |
| 3677 | static unsigned getHashValue(const Instruction *I) { |
| 3678 | assert(canHandle(I) && "Unknown instruction!" ); |
| 3679 | return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(), |
| 3680 | I->value_op_end())); |
| 3681 | } |
| 3682 | |
| 3683 | static bool isEqual(const Instruction *LHS, const Instruction *RHS) { |
| 3684 | if (LHS == getEmptyKey() || RHS == getEmptyKey() || |
| 3685 | LHS == getTombstoneKey() || RHS == getTombstoneKey()) |
| 3686 | return LHS == RHS; |
| 3687 | return LHS->isIdenticalTo(RHS); |
| 3688 | } |
| 3689 | }; |
| 3690 | |
| 3691 | } // end anonymous namespace |
| 3692 | |
| 3693 | ///Perform cse of induction variable instructions. |
| 3694 | static void cse(BasicBlock *BB) { |
| 3695 | // Perform simple cse. |
| 3696 | SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap; |
| 3697 | for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) { |
| 3698 | Instruction *In = &*I++; |
| 3699 | |
| 3700 | if (!CSEDenseMapInfo::canHandle(In)) |
| 3701 | continue; |
| 3702 | |
| 3703 | // Check if we can replace this instruction with any of the |
| 3704 | // visited instructions. |
| 3705 | if (Instruction *V = CSEMap.lookup(In)) { |
| 3706 | In->replaceAllUsesWith(V); |
| 3707 | In->eraseFromParent(); |
| 3708 | continue; |
| 3709 | } |
| 3710 | |
| 3711 | CSEMap[In] = In; |
| 3712 | } |
| 3713 | } |
| 3714 | |
| 3715 | InstructionCost |
| 3716 | LoopVectorizationCostModel::getVectorCallCost(CallInst *CI, ElementCount VF, |
| 3717 | bool &NeedToScalarize) { |
| 3718 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 3719 | Function *F = CI->getCalledFunction(); |
| 3720 | Type *ScalarRetTy = CI->getType(); |
| 3721 | SmallVector<Type *, 4> Tys, ScalarTys; |
| 3722 | for (auto &ArgOp : CI->arg_operands()) |
| 3723 | ScalarTys.push_back(ArgOp->getType()); |
| 3724 | |
| 3725 | // Estimate cost of scalarized vector call. The source operands are assumed |
| 3726 | // to be vectors, so we need to extract individual elements from there, |
| 3727 | // execute VF scalar calls, and then gather the result into the vector return |
| 3728 | // value. |
| 3729 | InstructionCost ScalarCallCost = |
| 3730 | TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys, TTI::TCK_RecipThroughput); |
| 3731 | if (VF.isScalar()) |
| 3732 | return ScalarCallCost; |
| 3733 | |
| 3734 | // Compute corresponding vector type for return value and arguments. |
| 3735 | Type *RetTy = ToVectorTy(ScalarRetTy, VF); |
| 3736 | for (Type *ScalarTy : ScalarTys) |
| 3737 | Tys.push_back(ToVectorTy(ScalarTy, VF)); |
| 3738 | |
| 3739 | // Compute costs of unpacking argument values for the scalar calls and |
| 3740 | // packing the return values to a vector. |
| 3741 | InstructionCost ScalarizationCost = getScalarizationOverhead(CI, VF); |
| 3742 | |
| 3743 | InstructionCost Cost = |
| 3744 | ScalarCallCost * VF.getKnownMinValue() + ScalarizationCost; |
| 3745 | |
| 3746 | // If we can't emit a vector call for this function, then the currently found |
| 3747 | // cost is the cost we need to return. |
| 3748 | NeedToScalarize = true; |
| 3749 | VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| 3750 | Function *VecFunc = VFDatabase(*CI).getVectorizedFunction(Shape); |
| 3751 | |
| 3752 | if (!TLI || CI->isNoBuiltin() || !VecFunc) |
| 3753 | return Cost; |
| 3754 | |
| 3755 | // If the corresponding vector cost is cheaper, return its cost. |
| 3756 | InstructionCost VectorCallCost = |
| 3757 | TTI.getCallInstrCost(nullptr, RetTy, Tys, TTI::TCK_RecipThroughput); |
| 3758 | if (VectorCallCost < Cost) { |
| 3759 | NeedToScalarize = false; |
| 3760 | Cost = VectorCallCost; |
| 3761 | } |
| 3762 | return Cost; |
| 3763 | } |
| 3764 | |
| 3765 | InstructionCost |
| 3766 | LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI, |
| 3767 | ElementCount VF) { |
| 3768 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 3769 | assert(ID && "Expected intrinsic call!" ); |
| 3770 | |
| 3771 | IntrinsicCostAttributes CostAttrs(ID, *CI, VF); |
| 3772 | return TTI.getIntrinsicInstrCost(CostAttrs, |
| 3773 | TargetTransformInfo::TCK_RecipThroughput); |
| 3774 | } |
| 3775 | |
| 3776 | static Type *smallestIntegerVectorType(Type *T1, Type *T2) { |
| 3777 | auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| 3778 | auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| 3779 | return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2; |
| 3780 | } |
| 3781 | |
| 3782 | static Type *largestIntegerVectorType(Type *T1, Type *T2) { |
| 3783 | auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| 3784 | auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| 3785 | return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2; |
| 3786 | } |
| 3787 | |
| 3788 | void InnerLoopVectorizer::truncateToMinimalBitwidths() { |
| 3789 | // For every instruction `I` in MinBWs, truncate the operands, create a |
| 3790 | // truncated version of `I` and reextend its result. InstCombine runs |
| 3791 | // later and will remove any ext/trunc pairs. |
| 3792 | SmallPtrSet<Value *, 4> Erased; |
| 3793 | for (const auto &KV : Cost->getMinimalBitwidths()) { |
| 3794 | // If the value wasn't vectorized, we must maintain the original scalar |
| 3795 | // type. The absence of the value from VectorLoopValueMap indicates that it |
| 3796 | // wasn't vectorized. |
| 3797 | if (!VectorLoopValueMap.hasAnyVectorValue(KV.first)) |
| 3798 | continue; |
| 3799 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 3800 | Value *I = getOrCreateVectorValue(KV.first, Part); |
| 3801 | if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I)) |
| 3802 | continue; |
| 3803 | Type *OriginalTy = I->getType(); |
| 3804 | Type *ScalarTruncatedTy = |
| 3805 | IntegerType::get(OriginalTy->getContext(), KV.second); |
| 3806 | auto *TruncatedTy = FixedVectorType::get( |
| 3807 | ScalarTruncatedTy, |
| 3808 | cast<FixedVectorType>(OriginalTy)->getNumElements()); |
| 3809 | if (TruncatedTy == OriginalTy) |
| 3810 | continue; |
| 3811 | |
| 3812 | IRBuilder<> B(cast<Instruction>(I)); |
| 3813 | auto ShrinkOperand = [&](Value *V) -> Value * { |
| 3814 | if (auto *ZI = dyn_cast<ZExtInst>(V)) |
| 3815 | if (ZI->getSrcTy() == TruncatedTy) |
| 3816 | return ZI->getOperand(0); |
| 3817 | return B.CreateZExtOrTrunc(V, TruncatedTy); |
| 3818 | }; |
| 3819 | |
| 3820 | // The actual instruction modification depends on the instruction type, |
| 3821 | // unfortunately. |
| 3822 | Value *NewI = nullptr; |
| 3823 | if (auto *BO = dyn_cast<BinaryOperator>(I)) { |
| 3824 | NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)), |
| 3825 | ShrinkOperand(BO->getOperand(1))); |
| 3826 | |
| 3827 | // Any wrapping introduced by shrinking this operation shouldn't be |
| 3828 | // considered undefined behavior. So, we can't unconditionally copy |
| 3829 | // arithmetic wrapping flags to NewI. |
| 3830 | cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false); |
| 3831 | } else if (auto *CI = dyn_cast<ICmpInst>(I)) { |
| 3832 | NewI = |
| 3833 | B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)), |
| 3834 | ShrinkOperand(CI->getOperand(1))); |
| 3835 | } else if (auto *SI = dyn_cast<SelectInst>(I)) { |
| 3836 | NewI = B.CreateSelect(SI->getCondition(), |
| 3837 | ShrinkOperand(SI->getTrueValue()), |
| 3838 | ShrinkOperand(SI->getFalseValue())); |
| 3839 | } else if (auto *CI = dyn_cast<CastInst>(I)) { |
| 3840 | switch (CI->getOpcode()) { |
| 3841 | default: |
| 3842 | llvm_unreachable("Unhandled cast!" ); |
| 3843 | case Instruction::Trunc: |
| 3844 | NewI = ShrinkOperand(CI->getOperand(0)); |
| 3845 | break; |
| 3846 | case Instruction::SExt: |
| 3847 | NewI = B.CreateSExtOrTrunc( |
| 3848 | CI->getOperand(0), |
| 3849 | smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| 3850 | break; |
| 3851 | case Instruction::ZExt: |
| 3852 | NewI = B.CreateZExtOrTrunc( |
| 3853 | CI->getOperand(0), |
| 3854 | smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| 3855 | break; |
| 3856 | } |
| 3857 | } else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) { |
| 3858 | auto Elements0 = cast<FixedVectorType>(SI->getOperand(0)->getType()) |
| 3859 | ->getNumElements(); |
| 3860 | auto *O0 = B.CreateZExtOrTrunc( |
| 3861 | SI->getOperand(0), |
| 3862 | FixedVectorType::get(ScalarTruncatedTy, Elements0)); |
| 3863 | auto Elements1 = cast<FixedVectorType>(SI->getOperand(1)->getType()) |
| 3864 | ->getNumElements(); |
| 3865 | auto *O1 = B.CreateZExtOrTrunc( |
| 3866 | SI->getOperand(1), |
| 3867 | FixedVectorType::get(ScalarTruncatedTy, Elements1)); |
| 3868 | |
| 3869 | NewI = B.CreateShuffleVector(O0, O1, SI->getShuffleMask()); |
| 3870 | } else if (isa<LoadInst>(I) || isa<PHINode>(I)) { |
| 3871 | // Don't do anything with the operands, just extend the result. |
| 3872 | continue; |
| 3873 | } else if (auto *IE = dyn_cast<InsertElementInst>(I)) { |
| 3874 | auto Elements = cast<FixedVectorType>(IE->getOperand(0)->getType()) |
| 3875 | ->getNumElements(); |
| 3876 | auto *O0 = B.CreateZExtOrTrunc( |
| 3877 | IE->getOperand(0), |
| 3878 | FixedVectorType::get(ScalarTruncatedTy, Elements)); |
| 3879 | auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy); |
| 3880 | NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2)); |
| 3881 | } else if (auto *EE = dyn_cast<ExtractElementInst>(I)) { |
| 3882 | auto Elements = cast<FixedVectorType>(EE->getOperand(0)->getType()) |
| 3883 | ->getNumElements(); |
| 3884 | auto *O0 = B.CreateZExtOrTrunc( |
| 3885 | EE->getOperand(0), |
| 3886 | FixedVectorType::get(ScalarTruncatedTy, Elements)); |
| 3887 | NewI = B.CreateExtractElement(O0, EE->getOperand(2)); |
| 3888 | } else { |
| 3889 | // If we don't know what to do, be conservative and don't do anything. |
| 3890 | continue; |
| 3891 | } |
| 3892 | |
| 3893 | // Lastly, extend the result. |
| 3894 | NewI->takeName(cast<Instruction>(I)); |
| 3895 | Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy); |
| 3896 | I->replaceAllUsesWith(Res); |
| 3897 | cast<Instruction>(I)->eraseFromParent(); |
| 3898 | Erased.insert(I); |
| 3899 | VectorLoopValueMap.resetVectorValue(KV.first, Part, Res); |
| 3900 | } |
| 3901 | } |
| 3902 | |
| 3903 | // We'll have created a bunch of ZExts that are now parentless. Clean up. |
| 3904 | for (const auto &KV : Cost->getMinimalBitwidths()) { |
| 3905 | // If the value wasn't vectorized, we must maintain the original scalar |
| 3906 | // type. The absence of the value from VectorLoopValueMap indicates that it |
| 3907 | // wasn't vectorized. |
| 3908 | if (!VectorLoopValueMap.hasAnyVectorValue(KV.first)) |
| 3909 | continue; |
| 3910 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 3911 | Value *I = getOrCreateVectorValue(KV.first, Part); |
| 3912 | ZExtInst *Inst = dyn_cast<ZExtInst>(I); |
| 3913 | if (Inst && Inst->use_empty()) { |
| 3914 | Value *NewI = Inst->getOperand(0); |
| 3915 | Inst->eraseFromParent(); |
| 3916 | VectorLoopValueMap.resetVectorValue(KV.first, Part, NewI); |
| 3917 | } |
| 3918 | } |
| 3919 | } |
| 3920 | } |
| 3921 | |
| 3922 | void InnerLoopVectorizer::fixVectorizedLoop() { |
| 3923 | // Insert truncates and extends for any truncated instructions as hints to |
| 3924 | // InstCombine. |
| 3925 | if (VF.isVector()) |
| 3926 | truncateToMinimalBitwidths(); |
| 3927 | |
| 3928 | // Fix widened non-induction PHIs by setting up the PHI operands. |
| 3929 | if (OrigPHIsToFix.size()) { |
| 3930 | assert(EnableVPlanNativePath && |
| 3931 | "Unexpected non-induction PHIs for fixup in non VPlan-native path" ); |
| 3932 | fixNonInductionPHIs(); |
| 3933 | } |
| 3934 | |
| 3935 | // At this point every instruction in the original loop is widened to a |
| 3936 | // vector form. Now we need to fix the recurrences in the loop. These PHI |
| 3937 | // nodes are currently empty because we did not want to introduce cycles. |
| 3938 | // This is the second stage of vectorizing recurrences. |
| 3939 | fixCrossIterationPHIs(); |
| 3940 | |
| 3941 | // Forget the original basic block. |
| 3942 | PSE.getSE()->forgetLoop(OrigLoop); |
| 3943 | |
| 3944 | // Fix-up external users of the induction variables. |
| 3945 | for (auto &Entry : Legal->getInductionVars()) |
| 3946 | fixupIVUsers(Entry.first, Entry.second, |
| 3947 | getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)), |
| 3948 | IVEndValues[Entry.first], LoopMiddleBlock); |
| 3949 | |
| 3950 | fixLCSSAPHIs(); |
| 3951 | for (Instruction *PI : PredicatedInstructions) |
| 3952 | sinkScalarOperands(&*PI); |
| 3953 | |
| 3954 | // Remove redundant induction instructions. |
| 3955 | cse(LoopVectorBody); |
| 3956 | |
| 3957 | // Set/update profile weights for the vector and remainder loops as original |
| 3958 | // loop iterations are now distributed among them. Note that original loop |
| 3959 | // represented by LoopScalarBody becomes remainder loop after vectorization. |
| 3960 | // |
| 3961 | // For cases like foldTailByMasking() and requiresScalarEpiloque() we may |
| 3962 | // end up getting slightly roughened result but that should be OK since |
| 3963 | // profile is not inherently precise anyway. Note also possible bypass of |
| 3964 | // vector code caused by legality checks is ignored, assigning all the weight |
| 3965 | // to the vector loop, optimistically. |
| 3966 | // |
| 3967 | // For scalable vectorization we can't know at compile time how many iterations |
| 3968 | // of the loop are handled in one vector iteration, so instead assume a pessimistic |
| 3969 | // vscale of '1'. |
| 3970 | setProfileInfoAfterUnrolling( |
| 3971 | LI->getLoopFor(LoopScalarBody), LI->getLoopFor(LoopVectorBody), |
| 3972 | LI->getLoopFor(LoopScalarBody), VF.getKnownMinValue() * UF); |
| 3973 | } |
| 3974 | |
| 3975 | void InnerLoopVectorizer::fixCrossIterationPHIs() { |
| 3976 | // In order to support recurrences we need to be able to vectorize Phi nodes. |
| 3977 | // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| 3978 | // stage #2: We now need to fix the recurrences by adding incoming edges to |
| 3979 | // the currently empty PHI nodes. At this point every instruction in the |
| 3980 | // original loop is widened to a vector form so we can use them to construct |
| 3981 | // the incoming edges. |
| 3982 | for (PHINode &Phi : OrigLoop->getHeader()->phis()) { |
| 3983 | // Handle first-order recurrences and reductions that need to be fixed. |
| 3984 | if (Legal->isFirstOrderRecurrence(&Phi)) |
| 3985 | fixFirstOrderRecurrence(&Phi); |
| 3986 | else if (Legal->isReductionVariable(&Phi)) |
| 3987 | fixReduction(&Phi); |
| 3988 | } |
| 3989 | } |
| 3990 | |
| 3991 | void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) { |
| 3992 | // This is the second phase of vectorizing first-order recurrences. An |
| 3993 | // overview of the transformation is described below. Suppose we have the |
| 3994 | // following loop. |
| 3995 | // |
| 3996 | // for (int i = 0; i < n; ++i) |
| 3997 | // b[i] = a[i] - a[i - 1]; |
| 3998 | // |
| 3999 | // There is a first-order recurrence on "a". For this loop, the shorthand |
| 4000 | // scalar IR looks like: |
| 4001 | // |
| 4002 | // scalar.ph: |
| 4003 | // s_init = a[-1] |
| 4004 | // br scalar.body |
| 4005 | // |
| 4006 | // scalar.body: |
| 4007 | // i = phi [0, scalar.ph], [i+1, scalar.body] |
| 4008 | // s1 = phi [s_init, scalar.ph], [s2, scalar.body] |
| 4009 | // s2 = a[i] |
| 4010 | // b[i] = s2 - s1 |
| 4011 | // br cond, scalar.body, ... |
| 4012 | // |
| 4013 | // In this example, s1 is a recurrence because it's value depends on the |
| 4014 | // previous iteration. In the first phase of vectorization, we created a |
| 4015 | // temporary value for s1. We now complete the vectorization and produce the |
| 4016 | // shorthand vector IR shown below (for VF = 4, UF = 1). |
| 4017 | // |
| 4018 | // vector.ph: |
| 4019 | // v_init = vector(..., ..., ..., a[-1]) |
| 4020 | // br vector.body |
| 4021 | // |
| 4022 | // vector.body |
| 4023 | // i = phi [0, vector.ph], [i+4, vector.body] |
| 4024 | // v1 = phi [v_init, vector.ph], [v2, vector.body] |
| 4025 | // v2 = a[i, i+1, i+2, i+3]; |
| 4026 | // v3 = vector(v1(3), v2(0, 1, 2)) |
| 4027 | // b[i, i+1, i+2, i+3] = v2 - v3 |
| 4028 | // br cond, vector.body, middle.block |
| 4029 | // |
| 4030 | // middle.block: |
| 4031 | // x = v2(3) |
| 4032 | // br scalar.ph |
| 4033 | // |
| 4034 | // scalar.ph: |
| 4035 | // s_init = phi [x, middle.block], [a[-1], otherwise] |
| 4036 | // br scalar.body |
| 4037 | // |
| 4038 | // After execution completes the vector loop, we extract the next value of |
| 4039 | // the recurrence (x) to use as the initial value in the scalar loop. |
| 4040 | |
| 4041 | // Get the original loop preheader and single loop latch. |
| 4042 | auto * = OrigLoop->getLoopPreheader(); |
| 4043 | auto *Latch = OrigLoop->getLoopLatch(); |
| 4044 | |
| 4045 | // Get the initial and previous values of the scalar recurrence. |
| 4046 | auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader); |
| 4047 | auto *Previous = Phi->getIncomingValueForBlock(Latch); |
| 4048 | |
| 4049 | // Create a vector from the initial value. |
| 4050 | auto *VectorInit = ScalarInit; |
| 4051 | if (VF.isVector()) { |
| 4052 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 4053 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 4054 | VectorInit = Builder.CreateInsertElement( |
| 4055 | PoisonValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit, |
| 4056 | Builder.getInt32(VF.getKnownMinValue() - 1), "vector.recur.init" ); |
| 4057 | } |
| 4058 | |
| 4059 | // We constructed a temporary phi node in the first phase of vectorization. |
| 4060 | // This phi node will eventually be deleted. |
| 4061 | Builder.SetInsertPoint( |
| 4062 | cast<Instruction>(VectorLoopValueMap.getVectorValue(Phi, 0))); |
| 4063 | |
| 4064 | // Create a phi node for the new recurrence. The current value will either be |
| 4065 | // the initial value inserted into a vector or loop-varying vector value. |
| 4066 | auto *VecPhi = Builder.CreatePHI(VectorInit->getType(), 2, "vector.recur" ); |
| 4067 | VecPhi->addIncoming(VectorInit, LoopVectorPreHeader); |
| 4068 | |
| 4069 | // Get the vectorized previous value of the last part UF - 1. It appears last |
| 4070 | // among all unrolled iterations, due to the order of their construction. |
| 4071 | Value *PreviousLastPart = getOrCreateVectorValue(Previous, UF - 1); |
| 4072 | |
| 4073 | // Find and set the insertion point after the previous value if it is an |
| 4074 | // instruction. |
| 4075 | BasicBlock::iterator InsertPt; |
| 4076 | // Note that the previous value may have been constant-folded so it is not |
| 4077 | // guaranteed to be an instruction in the vector loop. |
| 4078 | // FIXME: Loop invariant values do not form recurrences. We should deal with |
| 4079 | // them earlier. |
| 4080 | if (LI->getLoopFor(LoopVectorBody)->isLoopInvariant(PreviousLastPart)) |
| 4081 | InsertPt = LoopVectorBody->getFirstInsertionPt(); |
| 4082 | else { |
| 4083 | Instruction *PreviousInst = cast<Instruction>(PreviousLastPart); |
| 4084 | if (isa<PHINode>(PreviousLastPart)) |
| 4085 | // If the previous value is a phi node, we should insert after all the phi |
| 4086 | // nodes in the block containing the PHI to avoid breaking basic block |
| 4087 | // verification. Note that the basic block may be different to |
| 4088 | // LoopVectorBody, in case we predicate the loop. |
| 4089 | InsertPt = PreviousInst->getParent()->getFirstInsertionPt(); |
| 4090 | else |
| 4091 | InsertPt = ++PreviousInst->getIterator(); |
| 4092 | } |
| 4093 | Builder.SetInsertPoint(&*InsertPt); |
| 4094 | |
| 4095 | // We will construct a vector for the recurrence by combining the values for |
| 4096 | // the current and previous iterations. This is the required shuffle mask. |
| 4097 | assert(!VF.isScalable()); |
| 4098 | SmallVector<int, 8> ShuffleMask(VF.getKnownMinValue()); |
| 4099 | ShuffleMask[0] = VF.getKnownMinValue() - 1; |
| 4100 | for (unsigned I = 1; I < VF.getKnownMinValue(); ++I) |
| 4101 | ShuffleMask[I] = I + VF.getKnownMinValue() - 1; |
| 4102 | |
| 4103 | // The vector from which to take the initial value for the current iteration |
| 4104 | // (actual or unrolled). Initially, this is the vector phi node. |
| 4105 | Value *Incoming = VecPhi; |
| 4106 | |
| 4107 | // Shuffle the current and previous vector and update the vector parts. |
| 4108 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4109 | Value *PreviousPart = getOrCreateVectorValue(Previous, Part); |
| 4110 | Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part); |
| 4111 | auto *Shuffle = |
| 4112 | VF.isVector() |
| 4113 | ? Builder.CreateShuffleVector(Incoming, PreviousPart, ShuffleMask) |
| 4114 | : Incoming; |
| 4115 | PhiPart->replaceAllUsesWith(Shuffle); |
| 4116 | cast<Instruction>(PhiPart)->eraseFromParent(); |
| 4117 | VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle); |
| 4118 | Incoming = PreviousPart; |
| 4119 | } |
| 4120 | |
| 4121 | // Fix the latch value of the new recurrence in the vector loop. |
| 4122 | VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch()); |
| 4123 | |
| 4124 | // Extract the last vector element in the middle block. This will be the |
| 4125 | // initial value for the recurrence when jumping to the scalar loop. |
| 4126 | auto * = Incoming; |
| 4127 | if (VF.isVector()) { |
| 4128 | Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| 4129 | ExtractForScalar = Builder.CreateExtractElement( |
| 4130 | ExtractForScalar, Builder.getInt32(VF.getKnownMinValue() - 1), |
| 4131 | "vector.recur.extract" ); |
| 4132 | } |
| 4133 | // Extract the second last element in the middle block if the |
| 4134 | // Phi is used outside the loop. We need to extract the phi itself |
| 4135 | // and not the last element (the phi update in the current iteration). This |
| 4136 | // will be the value when jumping to the exit block from the LoopMiddleBlock, |
| 4137 | // when the scalar loop is not run at all. |
| 4138 | Value * = nullptr; |
| 4139 | if (VF.isVector()) |
| 4140 | ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement( |
| 4141 | Incoming, Builder.getInt32(VF.getKnownMinValue() - 2), |
| 4142 | "vector.recur.extract.for.phi" ); |
| 4143 | // When loop is unrolled without vectorizing, initialize |
| 4144 | // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of |
| 4145 | // `Incoming`. This is analogous to the vectorized case above: extracting the |
| 4146 | // second last element when VF > 1. |
| 4147 | else if (UF > 1) |
| 4148 | ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2); |
| 4149 | |
| 4150 | // Fix the initial value of the original recurrence in the scalar loop. |
| 4151 | Builder.SetInsertPoint(&*LoopScalarPreHeader->begin()); |
| 4152 | auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init" ); |
| 4153 | for (auto *BB : predecessors(LoopScalarPreHeader)) { |
| 4154 | auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit; |
| 4155 | Start->addIncoming(Incoming, BB); |
| 4156 | } |
| 4157 | |
| 4158 | Phi->setIncomingValueForBlock(LoopScalarPreHeader, Start); |
| 4159 | Phi->setName("scalar.recur" ); |
| 4160 | |
| 4161 | // Finally, fix users of the recurrence outside the loop. The users will need |
| 4162 | // either the last value of the scalar recurrence or the last value of the |
| 4163 | // vector recurrence we extracted in the middle block. Since the loop is in |
| 4164 | // LCSSA form, we just need to find all the phi nodes for the original scalar |
| 4165 | // recurrence in the exit block, and then add an edge for the middle block. |
| 4166 | // Note that LCSSA does not imply single entry when the original scalar loop |
| 4167 | // had multiple exiting edges (as we always run the last iteration in the |
| 4168 | // scalar epilogue); in that case, the exiting path through middle will be |
| 4169 | // dynamically dead and the value picked for the phi doesn't matter. |
| 4170 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| 4171 | if (any_of(LCSSAPhi.incoming_values(), |
| 4172 | [Phi](Value *V) { return V == Phi; })) |
| 4173 | LCSSAPhi.addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock); |
| 4174 | } |
| 4175 | |
| 4176 | void InnerLoopVectorizer::fixReduction(PHINode *Phi) { |
| 4177 | // Get it's reduction variable descriptor. |
| 4178 | assert(Legal->isReductionVariable(Phi) && |
| 4179 | "Unable to find the reduction variable" ); |
| 4180 | RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi]; |
| 4181 | |
| 4182 | RecurKind RK = RdxDesc.getRecurrenceKind(); |
| 4183 | TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue(); |
| 4184 | Instruction *LoopExitInst = RdxDesc.getLoopExitInstr(); |
| 4185 | setDebugLocFromInst(Builder, ReductionStartValue); |
| 4186 | bool IsInLoopReductionPhi = Cost->isInLoopReduction(Phi); |
| 4187 | |
| 4188 | // This is the vector-clone of the value that leaves the loop. |
| 4189 | Type *VecTy = getOrCreateVectorValue(LoopExitInst, 0)->getType(); |
| 4190 | |
| 4191 | // Wrap flags are in general invalid after vectorization, clear them. |
| 4192 | clearReductionWrapFlags(RdxDesc); |
| 4193 | |
| 4194 | // Fix the vector-loop phi. |
| 4195 | |
| 4196 | // Reductions do not have to start at zero. They can start with |
| 4197 | // any loop invariant values. |
| 4198 | BasicBlock *Latch = OrigLoop->getLoopLatch(); |
| 4199 | Value *LoopVal = Phi->getIncomingValueForBlock(Latch); |
| 4200 | |
| 4201 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4202 | Value *VecRdxPhi = getOrCreateVectorValue(Phi, Part); |
| 4203 | Value *Val = getOrCreateVectorValue(LoopVal, Part); |
| 4204 | cast<PHINode>(VecRdxPhi) |
| 4205 | ->addIncoming(Val, LI->getLoopFor(LoopVectorBody)->getLoopLatch()); |
| 4206 | } |
| 4207 | |
| 4208 | // Before each round, move the insertion point right between |
| 4209 | // the PHIs and the values we are going to write. |
| 4210 | // This allows us to write both PHINodes and the extractelement |
| 4211 | // instructions. |
| 4212 | Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| 4213 | |
| 4214 | setDebugLocFromInst(Builder, LoopExitInst); |
| 4215 | |
| 4216 | // If tail is folded by masking, the vector value to leave the loop should be |
| 4217 | // a Select choosing between the vectorized LoopExitInst and vectorized Phi, |
| 4218 | // instead of the former. For an inloop reduction the reduction will already |
| 4219 | // be predicated, and does not need to be handled here. |
| 4220 | if (Cost->foldTailByMasking() && !IsInLoopReductionPhi) { |
| 4221 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4222 | Value *VecLoopExitInst = |
| 4223 | VectorLoopValueMap.getVectorValue(LoopExitInst, Part); |
| 4224 | Value *Sel = nullptr; |
| 4225 | for (User *U : VecLoopExitInst->users()) { |
| 4226 | if (isa<SelectInst>(U)) { |
| 4227 | assert(!Sel && "Reduction exit feeding two selects" ); |
| 4228 | Sel = U; |
| 4229 | } else |
| 4230 | assert(isa<PHINode>(U) && "Reduction exit must feed Phi's or select" ); |
| 4231 | } |
| 4232 | assert(Sel && "Reduction exit feeds no select" ); |
| 4233 | VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, Sel); |
| 4234 | |
| 4235 | // If the target can create a predicated operator for the reduction at no |
| 4236 | // extra cost in the loop (for example a predicated vadd), it can be |
| 4237 | // cheaper for the select to remain in the loop than be sunk out of it, |
| 4238 | // and so use the select value for the phi instead of the old |
| 4239 | // LoopExitValue. |
| 4240 | RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi]; |
| 4241 | if (PreferPredicatedReductionSelect || |
| 4242 | TTI->preferPredicatedReductionSelect( |
| 4243 | RdxDesc.getOpcode(), Phi->getType(), |
| 4244 | TargetTransformInfo::ReductionFlags())) { |
| 4245 | auto *VecRdxPhi = cast<PHINode>(getOrCreateVectorValue(Phi, Part)); |
| 4246 | VecRdxPhi->setIncomingValueForBlock( |
| 4247 | LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel); |
| 4248 | } |
| 4249 | } |
| 4250 | } |
| 4251 | |
| 4252 | // If the vector reduction can be performed in a smaller type, we truncate |
| 4253 | // then extend the loop exit value to enable InstCombine to evaluate the |
| 4254 | // entire expression in the smaller type. |
| 4255 | if (VF.isVector() && Phi->getType() != RdxDesc.getRecurrenceType()) { |
| 4256 | assert(!IsInLoopReductionPhi && "Unexpected truncated inloop reduction!" ); |
| 4257 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 4258 | Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF); |
| 4259 | Builder.SetInsertPoint( |
| 4260 | LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator()); |
| 4261 | VectorParts RdxParts(UF); |
| 4262 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4263 | RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part); |
| 4264 | Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| 4265 | Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy) |
| 4266 | : Builder.CreateZExt(Trunc, VecTy); |
| 4267 | for (Value::user_iterator UI = RdxParts[Part]->user_begin(); |
| 4268 | UI != RdxParts[Part]->user_end();) |
| 4269 | if (*UI != Trunc) { |
| 4270 | (*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd); |
| 4271 | RdxParts[Part] = Extnd; |
| 4272 | } else { |
| 4273 | ++UI; |
| 4274 | } |
| 4275 | } |
| 4276 | Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| 4277 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4278 | RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| 4279 | VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, RdxParts[Part]); |
| 4280 | } |
| 4281 | } |
| 4282 | |
| 4283 | // Reduce all of the unrolled parts into a single vector. |
| 4284 | Value *ReducedPartRdx = VectorLoopValueMap.getVectorValue(LoopExitInst, 0); |
| 4285 | unsigned Op = RecurrenceDescriptor::getOpcode(RK); |
| 4286 | |
| 4287 | // The middle block terminator has already been assigned a DebugLoc here (the |
| 4288 | // OrigLoop's single latch terminator). We want the whole middle block to |
| 4289 | // appear to execute on this line because: (a) it is all compiler generated, |
| 4290 | // (b) these instructions are always executed after evaluating the latch |
| 4291 | // conditional branch, and (c) other passes may add new predecessors which |
| 4292 | // terminate on this line. This is the easiest way to ensure we don't |
| 4293 | // accidentally cause an extra step back into the loop while debugging. |
| 4294 | setDebugLocFromInst(Builder, LoopMiddleBlock->getTerminator()); |
| 4295 | for (unsigned Part = 1; Part < UF; ++Part) { |
| 4296 | Value *RdxPart = VectorLoopValueMap.getVectorValue(LoopExitInst, Part); |
| 4297 | if (Op != Instruction::ICmp && Op != Instruction::FCmp) |
| 4298 | // Floating point operations had to be 'fast' to enable the reduction. |
| 4299 | ReducedPartRdx = addFastMathFlag( |
| 4300 | Builder.CreateBinOp((Instruction::BinaryOps)Op, RdxPart, |
| 4301 | ReducedPartRdx, "bin.rdx" ), |
| 4302 | RdxDesc.getFastMathFlags()); |
| 4303 | else |
| 4304 | ReducedPartRdx = createMinMaxOp(Builder, RK, ReducedPartRdx, RdxPart); |
| 4305 | } |
| 4306 | |
| 4307 | // Create the reduction after the loop. Note that inloop reductions create the |
| 4308 | // target reduction in the loop using a Reduction recipe. |
| 4309 | if (VF.isVector() && !IsInLoopReductionPhi) { |
| 4310 | ReducedPartRdx = |
| 4311 | createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx); |
| 4312 | // If the reduction can be performed in a smaller type, we need to extend |
| 4313 | // the reduction to the wider type before we branch to the original loop. |
| 4314 | if (Phi->getType() != RdxDesc.getRecurrenceType()) |
| 4315 | ReducedPartRdx = |
| 4316 | RdxDesc.isSigned() |
| 4317 | ? Builder.CreateSExt(ReducedPartRdx, Phi->getType()) |
| 4318 | : Builder.CreateZExt(ReducedPartRdx, Phi->getType()); |
| 4319 | } |
| 4320 | |
| 4321 | // Create a phi node that merges control-flow from the backedge-taken check |
| 4322 | // block and the middle block. |
| 4323 | PHINode *BCBlockPhi = PHINode::Create(Phi->getType(), 2, "bc.merge.rdx" , |
| 4324 | LoopScalarPreHeader->getTerminator()); |
| 4325 | for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) |
| 4326 | BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]); |
| 4327 | BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| 4328 | |
| 4329 | // Now, we need to fix the users of the reduction variable |
| 4330 | // inside and outside of the scalar remainder loop. |
| 4331 | |
| 4332 | // We know that the loop is in LCSSA form. We need to update the PHI nodes |
| 4333 | // in the exit blocks. See comment on analogous loop in |
| 4334 | // fixFirstOrderRecurrence for a more complete explaination of the logic. |
| 4335 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| 4336 | if (any_of(LCSSAPhi.incoming_values(), |
| 4337 | [LoopExitInst](Value *V) { return V == LoopExitInst; })) |
| 4338 | LCSSAPhi.addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| 4339 | |
| 4340 | // Fix the scalar loop reduction variable with the incoming reduction sum |
| 4341 | // from the vector body and from the backedge value. |
| 4342 | int IncomingEdgeBlockIdx = |
| 4343 | Phi->getBasicBlockIndex(OrigLoop->getLoopLatch()); |
| 4344 | assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index" ); |
| 4345 | // Pick the other block. |
| 4346 | int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); |
| 4347 | Phi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi); |
| 4348 | Phi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst); |
| 4349 | } |
| 4350 | |
| 4351 | void InnerLoopVectorizer::clearReductionWrapFlags( |
| 4352 | RecurrenceDescriptor &RdxDesc) { |
| 4353 | RecurKind RK = RdxDesc.getRecurrenceKind(); |
| 4354 | if (RK != RecurKind::Add && RK != RecurKind::Mul) |
| 4355 | return; |
| 4356 | |
| 4357 | Instruction *LoopExitInstr = RdxDesc.getLoopExitInstr(); |
| 4358 | assert(LoopExitInstr && "null loop exit instruction" ); |
| 4359 | SmallVector<Instruction *, 8> Worklist; |
| 4360 | SmallPtrSet<Instruction *, 8> Visited; |
| 4361 | Worklist.push_back(LoopExitInstr); |
| 4362 | Visited.insert(LoopExitInstr); |
| 4363 | |
| 4364 | while (!Worklist.empty()) { |
| 4365 | Instruction *Cur = Worklist.pop_back_val(); |
| 4366 | if (isa<OverflowingBinaryOperator>(Cur)) |
| 4367 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4368 | Value *V = getOrCreateVectorValue(Cur, Part); |
| 4369 | cast<Instruction>(V)->dropPoisonGeneratingFlags(); |
| 4370 | } |
| 4371 | |
| 4372 | for (User *U : Cur->users()) { |
| 4373 | Instruction *UI = cast<Instruction>(U); |
| 4374 | if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) && |
| 4375 | Visited.insert(UI).second) |
| 4376 | Worklist.push_back(UI); |
| 4377 | } |
| 4378 | } |
| 4379 | } |
| 4380 | |
| 4381 | void InnerLoopVectorizer::fixLCSSAPHIs() { |
| 4382 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) { |
| 4383 | if (LCSSAPhi.getBasicBlockIndex(LoopMiddleBlock) != -1) |
| 4384 | // Some phis were already hand updated by the reduction and recurrence |
| 4385 | // code above, leave them alone. |
| 4386 | continue; |
| 4387 | |
| 4388 | auto *IncomingValue = LCSSAPhi.getIncomingValue(0); |
| 4389 | // Non-instruction incoming values will have only one value. |
| 4390 | unsigned LastLane = 0; |
| 4391 | if (isa<Instruction>(IncomingValue)) |
| 4392 | LastLane = Cost->isUniformAfterVectorization( |
| 4393 | cast<Instruction>(IncomingValue), VF) |
| 4394 | ? 0 |
| 4395 | : VF.getKnownMinValue() - 1; |
| 4396 | assert((!VF.isScalable() || LastLane == 0) && |
| 4397 | "scalable vectors dont support non-uniform scalars yet" ); |
| 4398 | // Can be a loop invariant incoming value or the last scalar value to be |
| 4399 | // extracted from the vectorized loop. |
| 4400 | Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| 4401 | Value *lastIncomingValue = |
| 4402 | getOrCreateScalarValue(IncomingValue, { UF - 1, LastLane }); |
| 4403 | LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock); |
| 4404 | } |
| 4405 | } |
| 4406 | |
| 4407 | void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) { |
| 4408 | // The basic block and loop containing the predicated instruction. |
| 4409 | auto *PredBB = PredInst->getParent(); |
| 4410 | auto *VectorLoop = LI->getLoopFor(PredBB); |
| 4411 | |
| 4412 | // Initialize a worklist with the operands of the predicated instruction. |
| 4413 | SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end()); |
| 4414 | |
| 4415 | // Holds instructions that we need to analyze again. An instruction may be |
| 4416 | // reanalyzed if we don't yet know if we can sink it or not. |
| 4417 | SmallVector<Instruction *, 8> InstsToReanalyze; |
| 4418 | |
| 4419 | // Returns true if a given use occurs in the predicated block. Phi nodes use |
| 4420 | // their operands in their corresponding predecessor blocks. |
| 4421 | auto isBlockOfUsePredicated = [&](Use &U) -> bool { |
| 4422 | auto *I = cast<Instruction>(U.getUser()); |
| 4423 | BasicBlock *BB = I->getParent(); |
| 4424 | if (auto *Phi = dyn_cast<PHINode>(I)) |
| 4425 | BB = Phi->getIncomingBlock( |
| 4426 | PHINode::getIncomingValueNumForOperand(U.getOperandNo())); |
| 4427 | return BB == PredBB; |
| 4428 | }; |
| 4429 | |
| 4430 | // Iteratively sink the scalarized operands of the predicated instruction |
| 4431 | // into the block we created for it. When an instruction is sunk, it's |
| 4432 | // operands are then added to the worklist. The algorithm ends after one pass |
| 4433 | // through the worklist doesn't sink a single instruction. |
| 4434 | bool Changed; |
| 4435 | do { |
| 4436 | // Add the instructions that need to be reanalyzed to the worklist, and |
| 4437 | // reset the changed indicator. |
| 4438 | Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end()); |
| 4439 | InstsToReanalyze.clear(); |
| 4440 | Changed = false; |
| 4441 | |
| 4442 | while (!Worklist.empty()) { |
| 4443 | auto *I = dyn_cast<Instruction>(Worklist.pop_back_val()); |
| 4444 | |
| 4445 | // We can't sink an instruction if it is a phi node, is already in the |
| 4446 | // predicated block, is not in the loop, or may have side effects. |
| 4447 | if (!I || isa<PHINode>(I) || I->getParent() == PredBB || |
| 4448 | !VectorLoop->contains(I) || I->mayHaveSideEffects()) |
| 4449 | continue; |
| 4450 | |
| 4451 | // It's legal to sink the instruction if all its uses occur in the |
| 4452 | // predicated block. Otherwise, there's nothing to do yet, and we may |
| 4453 | // need to reanalyze the instruction. |
| 4454 | if (!llvm::all_of(I->uses(), isBlockOfUsePredicated)) { |
| 4455 | InstsToReanalyze.push_back(I); |
| 4456 | continue; |
| 4457 | } |
| 4458 | |
| 4459 | // Move the instruction to the beginning of the predicated block, and add |
| 4460 | // it's operands to the worklist. |
| 4461 | I->moveBefore(&*PredBB->getFirstInsertionPt()); |
| 4462 | Worklist.insert(I->op_begin(), I->op_end()); |
| 4463 | |
| 4464 | // The sinking may have enabled other instructions to be sunk, so we will |
| 4465 | // need to iterate. |
| 4466 | Changed = true; |
| 4467 | } |
| 4468 | } while (Changed); |
| 4469 | } |
| 4470 | |
| 4471 | void InnerLoopVectorizer::fixNonInductionPHIs() { |
| 4472 | for (PHINode *OrigPhi : OrigPHIsToFix) { |
| 4473 | PHINode *NewPhi = |
| 4474 | cast<PHINode>(VectorLoopValueMap.getVectorValue(OrigPhi, 0)); |
| 4475 | unsigned NumIncomingValues = OrigPhi->getNumIncomingValues(); |
| 4476 | |
| 4477 | SmallVector<BasicBlock *, 2> ScalarBBPredecessors( |
| 4478 | predecessors(OrigPhi->getParent())); |
| 4479 | SmallVector<BasicBlock *, 2> VectorBBPredecessors( |
| 4480 | predecessors(NewPhi->getParent())); |
| 4481 | assert(ScalarBBPredecessors.size() == VectorBBPredecessors.size() && |
| 4482 | "Scalar and Vector BB should have the same number of predecessors" ); |
| 4483 | |
| 4484 | // The insertion point in Builder may be invalidated by the time we get |
| 4485 | // here. Force the Builder insertion point to something valid so that we do |
| 4486 | // not run into issues during insertion point restore in |
| 4487 | // getOrCreateVectorValue calls below. |
| 4488 | Builder.SetInsertPoint(NewPhi); |
| 4489 | |
| 4490 | // The predecessor order is preserved and we can rely on mapping between |
| 4491 | // scalar and vector block predecessors. |
| 4492 | for (unsigned i = 0; i < NumIncomingValues; ++i) { |
| 4493 | BasicBlock *NewPredBB = VectorBBPredecessors[i]; |
| 4494 | |
| 4495 | // When looking up the new scalar/vector values to fix up, use incoming |
| 4496 | // values from original phi. |
| 4497 | Value *ScIncV = |
| 4498 | OrigPhi->getIncomingValueForBlock(ScalarBBPredecessors[i]); |
| 4499 | |
| 4500 | // Scalar incoming value may need a broadcast |
| 4501 | Value *NewIncV = getOrCreateVectorValue(ScIncV, 0); |
| 4502 | NewPhi->addIncoming(NewIncV, NewPredBB); |
| 4503 | } |
| 4504 | } |
| 4505 | } |
| 4506 | |
| 4507 | void InnerLoopVectorizer::widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, |
| 4508 | VPUser &Operands, unsigned UF, |
| 4509 | ElementCount VF, bool IsPtrLoopInvariant, |
| 4510 | SmallBitVector &IsIndexLoopInvariant, |
| 4511 | VPTransformState &State) { |
| 4512 | // Construct a vector GEP by widening the operands of the scalar GEP as |
| 4513 | // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP |
| 4514 | // results in a vector of pointers when at least one operand of the GEP |
| 4515 | // is vector-typed. Thus, to keep the representation compact, we only use |
| 4516 | // vector-typed operands for loop-varying values. |
| 4517 | |
| 4518 | if (VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) { |
| 4519 | // If we are vectorizing, but the GEP has only loop-invariant operands, |
| 4520 | // the GEP we build (by only using vector-typed operands for |
| 4521 | // loop-varying values) would be a scalar pointer. Thus, to ensure we |
| 4522 | // produce a vector of pointers, we need to either arbitrarily pick an |
| 4523 | // operand to broadcast, or broadcast a clone of the original GEP. |
| 4524 | // Here, we broadcast a clone of the original. |
| 4525 | // |
| 4526 | // TODO: If at some point we decide to scalarize instructions having |
| 4527 | // loop-invariant operands, this special case will no longer be |
| 4528 | // required. We would add the scalarization decision to |
| 4529 | // collectLoopScalars() and teach getVectorValue() to broadcast |
| 4530 | // the lane-zero scalar value. |
| 4531 | auto *Clone = Builder.Insert(GEP->clone()); |
| 4532 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4533 | Value *EntryPart = Builder.CreateVectorSplat(VF, Clone); |
| 4534 | State.set(VPDef, GEP, EntryPart, Part); |
| 4535 | addMetadata(EntryPart, GEP); |
| 4536 | } |
| 4537 | } else { |
| 4538 | // If the GEP has at least one loop-varying operand, we are sure to |
| 4539 | // produce a vector of pointers. But if we are only unrolling, we want |
| 4540 | // to produce a scalar GEP for each unroll part. Thus, the GEP we |
| 4541 | // produce with the code below will be scalar (if VF == 1) or vector |
| 4542 | // (otherwise). Note that for the unroll-only case, we still maintain |
| 4543 | // values in the vector mapping with initVector, as we do for other |
| 4544 | // instructions. |
| 4545 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4546 | // The pointer operand of the new GEP. If it's loop-invariant, we |
| 4547 | // won't broadcast it. |
| 4548 | auto *Ptr = IsPtrLoopInvariant ? State.get(Operands.getOperand(0), {0, 0}) |
| 4549 | : State.get(Operands.getOperand(0), Part); |
| 4550 | |
| 4551 | // Collect all the indices for the new GEP. If any index is |
| 4552 | // loop-invariant, we won't broadcast it. |
| 4553 | SmallVector<Value *, 4> Indices; |
| 4554 | for (unsigned I = 1, E = Operands.getNumOperands(); I < E; I++) { |
| 4555 | VPValue *Operand = Operands.getOperand(I); |
| 4556 | if (IsIndexLoopInvariant[I - 1]) |
| 4557 | Indices.push_back(State.get(Operand, {0, 0})); |
| 4558 | else |
| 4559 | Indices.push_back(State.get(Operand, Part)); |
| 4560 | } |
| 4561 | |
| 4562 | // Create the new GEP. Note that this GEP may be a scalar if VF == 1, |
| 4563 | // but it should be a vector, otherwise. |
| 4564 | auto *NewGEP = |
| 4565 | GEP->isInBounds() |
| 4566 | ? Builder.CreateInBoundsGEP(GEP->getSourceElementType(), Ptr, |
| 4567 | Indices) |
| 4568 | : Builder.CreateGEP(GEP->getSourceElementType(), Ptr, Indices); |
| 4569 | assert((VF.isScalar() || NewGEP->getType()->isVectorTy()) && |
| 4570 | "NewGEP is not a pointer vector" ); |
| 4571 | State.set(VPDef, GEP, NewGEP, Part); |
| 4572 | addMetadata(NewGEP, GEP); |
| 4573 | } |
| 4574 | } |
| 4575 | } |
| 4576 | |
| 4577 | void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, |
| 4578 | RecurrenceDescriptor *RdxDesc, |
| 4579 | Value *StartV, unsigned UF, |
| 4580 | ElementCount VF) { |
| 4581 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 4582 | PHINode *P = cast<PHINode>(PN); |
| 4583 | if (EnableVPlanNativePath) { |
| 4584 | // Currently we enter here in the VPlan-native path for non-induction |
| 4585 | // PHIs where all control flow is uniform. We simply widen these PHIs. |
| 4586 | // Create a vector phi with no operands - the vector phi operands will be |
| 4587 | // set at the end of vector code generation. |
| 4588 | Type *VecTy = |
| 4589 | (VF.isScalar()) ? PN->getType() : VectorType::get(PN->getType(), VF); |
| 4590 | Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi" ); |
| 4591 | VectorLoopValueMap.setVectorValue(P, 0, VecPhi); |
| 4592 | OrigPHIsToFix.push_back(P); |
| 4593 | |
| 4594 | return; |
| 4595 | } |
| 4596 | |
| 4597 | assert(PN->getParent() == OrigLoop->getHeader() && |
| 4598 | "Non-header phis should have been handled elsewhere" ); |
| 4599 | |
| 4600 | // In order to support recurrences we need to be able to vectorize Phi nodes. |
| 4601 | // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| 4602 | // stage #1: We create a new vector PHI node with no incoming edges. We'll use |
| 4603 | // this value when we vectorize all of the instructions that use the PHI. |
| 4604 | if (RdxDesc || Legal->isFirstOrderRecurrence(P)) { |
| 4605 | Value *Iden = nullptr; |
| 4606 | bool ScalarPHI = |
| 4607 | (VF.isScalar()) || Cost->isInLoopReduction(cast<PHINode>(PN)); |
| 4608 | Type *VecTy = |
| 4609 | ScalarPHI ? PN->getType() : VectorType::get(PN->getType(), VF); |
| 4610 | |
| 4611 | if (RdxDesc) { |
| 4612 | assert(Legal->isReductionVariable(P) && StartV && |
| 4613 | "RdxDesc should only be set for reduction variables; in that case " |
| 4614 | "a StartV is also required" ); |
| 4615 | RecurKind RK = RdxDesc->getRecurrenceKind(); |
| 4616 | if (RecurrenceDescriptor::isMinMaxRecurrenceKind(RK)) { |
| 4617 | // MinMax reduction have the start value as their identify. |
| 4618 | if (ScalarPHI) { |
| 4619 | Iden = StartV; |
| 4620 | } else { |
| 4621 | IRBuilderBase::InsertPointGuard IPBuilder(Builder); |
| 4622 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 4623 | StartV = Iden = Builder.CreateVectorSplat(VF, StartV, "minmax.ident" ); |
| 4624 | } |
| 4625 | } else { |
| 4626 | Constant *IdenC = RecurrenceDescriptor::getRecurrenceIdentity( |
| 4627 | RK, VecTy->getScalarType()); |
| 4628 | Iden = IdenC; |
| 4629 | |
| 4630 | if (!ScalarPHI) { |
| 4631 | Iden = ConstantVector::getSplat(VF, IdenC); |
| 4632 | IRBuilderBase::InsertPointGuard IPBuilder(Builder); |
| 4633 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 4634 | Constant *Zero = Builder.getInt32(0); |
| 4635 | StartV = Builder.CreateInsertElement(Iden, StartV, Zero); |
| 4636 | } |
| 4637 | } |
| 4638 | } |
| 4639 | |
| 4640 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4641 | // This is phase one of vectorizing PHIs. |
| 4642 | Value *EntryPart = PHINode::Create( |
| 4643 | VecTy, 2, "vec.phi" , &*LoopVectorBody->getFirstInsertionPt()); |
| 4644 | VectorLoopValueMap.setVectorValue(P, Part, EntryPart); |
| 4645 | if (StartV) { |
| 4646 | // Make sure to add the reduction start value only to the |
| 4647 | // first unroll part. |
| 4648 | Value *StartVal = (Part == 0) ? StartV : Iden; |
| 4649 | cast<PHINode>(EntryPart)->addIncoming(StartVal, LoopVectorPreHeader); |
| 4650 | } |
| 4651 | } |
| 4652 | return; |
| 4653 | } |
| 4654 | |
| 4655 | assert(!Legal->isReductionVariable(P) && |
| 4656 | "reductions should be handled above" ); |
| 4657 | |
| 4658 | setDebugLocFromInst(Builder, P); |
| 4659 | |
| 4660 | // This PHINode must be an induction variable. |
| 4661 | // Make sure that we know about it. |
| 4662 | assert(Legal->getInductionVars().count(P) && "Not an induction variable" ); |
| 4663 | |
| 4664 | InductionDescriptor II = Legal->getInductionVars().lookup(P); |
| 4665 | const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 4666 | |
| 4667 | // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| 4668 | // which can be found from the original scalar operations. |
| 4669 | switch (II.getKind()) { |
| 4670 | case InductionDescriptor::IK_NoInduction: |
| 4671 | llvm_unreachable("Unknown induction" ); |
| 4672 | case InductionDescriptor::IK_IntInduction: |
| 4673 | case InductionDescriptor::IK_FpInduction: |
| 4674 | llvm_unreachable("Integer/fp induction is handled elsewhere." ); |
| 4675 | case InductionDescriptor::IK_PtrInduction: { |
| 4676 | // Handle the pointer induction variable case. |
| 4677 | assert(P->getType()->isPointerTy() && "Unexpected type." ); |
| 4678 | |
| 4679 | if (Cost->isScalarAfterVectorization(P, VF)) { |
| 4680 | // This is the normalized GEP that starts counting at zero. |
| 4681 | Value *PtrInd = |
| 4682 | Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType()); |
| 4683 | // Determine the number of scalars we need to generate for each unroll |
| 4684 | // iteration. If the instruction is uniform, we only need to generate the |
| 4685 | // first lane. Otherwise, we generate all VF values. |
| 4686 | unsigned Lanes = |
| 4687 | Cost->isUniformAfterVectorization(P, VF) ? 1 : VF.getKnownMinValue(); |
| 4688 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4689 | for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| 4690 | Constant *Idx = ConstantInt::get(PtrInd->getType(), |
| 4691 | Lane + Part * VF.getKnownMinValue()); |
| 4692 | Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx); |
| 4693 | Value *SclrGep = |
| 4694 | emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II); |
| 4695 | SclrGep->setName("next.gep" ); |
| 4696 | VectorLoopValueMap.setScalarValue(P, {Part, Lane}, SclrGep); |
| 4697 | } |
| 4698 | } |
| 4699 | return; |
| 4700 | } |
| 4701 | assert(isa<SCEVConstant>(II.getStep()) && |
| 4702 | "Induction step not a SCEV constant!" ); |
| 4703 | Type *PhiType = II.getStep()->getType(); |
| 4704 | |
| 4705 | // Build a pointer phi |
| 4706 | Value *ScalarStartValue = II.getStartValue(); |
| 4707 | Type *ScStValueType = ScalarStartValue->getType(); |
| 4708 | PHINode *NewPointerPhi = |
| 4709 | PHINode::Create(ScStValueType, 2, "pointer.phi" , Induction); |
| 4710 | NewPointerPhi->addIncoming(ScalarStartValue, LoopVectorPreHeader); |
| 4711 | |
| 4712 | // A pointer induction, performed by using a gep |
| 4713 | BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| 4714 | Instruction *InductionLoc = LoopLatch->getTerminator(); |
| 4715 | const SCEV *ScalarStep = II.getStep(); |
| 4716 | SCEVExpander Exp(*PSE.getSE(), DL, "induction" ); |
| 4717 | Value *ScalarStepValue = |
| 4718 | Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc); |
| 4719 | Value *InductionGEP = GetElementPtrInst::Create( |
| 4720 | ScStValueType->getPointerElementType(), NewPointerPhi, |
| 4721 | Builder.CreateMul( |
| 4722 | ScalarStepValue, |
| 4723 | ConstantInt::get(PhiType, VF.getKnownMinValue() * UF)), |
| 4724 | "ptr.ind" , InductionLoc); |
| 4725 | NewPointerPhi->addIncoming(InductionGEP, LoopLatch); |
| 4726 | |
| 4727 | // Create UF many actual address geps that use the pointer |
| 4728 | // phi as base and a vectorized version of the step value |
| 4729 | // (<step*0, ..., step*N>) as offset. |
| 4730 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4731 | SmallVector<Constant *, 8> Indices; |
| 4732 | // Create a vector of consecutive numbers from zero to VF. |
| 4733 | for (unsigned i = 0; i < VF.getKnownMinValue(); ++i) |
| 4734 | Indices.push_back( |
| 4735 | ConstantInt::get(PhiType, i + Part * VF.getKnownMinValue())); |
| 4736 | Constant *StartOffset = ConstantVector::get(Indices); |
| 4737 | |
| 4738 | Value *GEP = Builder.CreateGEP( |
| 4739 | ScStValueType->getPointerElementType(), NewPointerPhi, |
| 4740 | Builder.CreateMul( |
| 4741 | StartOffset, |
| 4742 | Builder.CreateVectorSplat(VF.getKnownMinValue(), ScalarStepValue), |
| 4743 | "vector.gep" )); |
| 4744 | VectorLoopValueMap.setVectorValue(P, Part, GEP); |
| 4745 | } |
| 4746 | } |
| 4747 | } |
| 4748 | } |
| 4749 | |
| 4750 | /// A helper function for checking whether an integer division-related |
| 4751 | /// instruction may divide by zero (in which case it must be predicated if |
| 4752 | /// executed conditionally in the scalar code). |
| 4753 | /// TODO: It may be worthwhile to generalize and check isKnownNonZero(). |
| 4754 | /// Non-zero divisors that are non compile-time constants will not be |
| 4755 | /// converted into multiplication, so we will still end up scalarizing |
| 4756 | /// the division, but can do so w/o predication. |
| 4757 | static bool mayDivideByZero(Instruction &I) { |
| 4758 | assert((I.getOpcode() == Instruction::UDiv || |
| 4759 | I.getOpcode() == Instruction::SDiv || |
| 4760 | I.getOpcode() == Instruction::URem || |
| 4761 | I.getOpcode() == Instruction::SRem) && |
| 4762 | "Unexpected instruction" ); |
| 4763 | Value *Divisor = I.getOperand(1); |
| 4764 | auto *CInt = dyn_cast<ConstantInt>(Divisor); |
| 4765 | return !CInt || CInt->isZero(); |
| 4766 | } |
| 4767 | |
| 4768 | void InnerLoopVectorizer::widenInstruction(Instruction &I, VPValue *Def, |
| 4769 | VPUser &User, |
| 4770 | VPTransformState &State) { |
| 4771 | switch (I.getOpcode()) { |
| 4772 | case Instruction::Call: |
| 4773 | case Instruction::Br: |
| 4774 | case Instruction::PHI: |
| 4775 | case Instruction::GetElementPtr: |
| 4776 | case Instruction::Select: |
| 4777 | llvm_unreachable("This instruction is handled by a different recipe." ); |
| 4778 | case Instruction::UDiv: |
| 4779 | case Instruction::SDiv: |
| 4780 | case Instruction::SRem: |
| 4781 | case Instruction::URem: |
| 4782 | case Instruction::Add: |
| 4783 | case Instruction::FAdd: |
| 4784 | case Instruction::Sub: |
| 4785 | case Instruction::FSub: |
| 4786 | case Instruction::FNeg: |
| 4787 | case Instruction::Mul: |
| 4788 | case Instruction::FMul: |
| 4789 | case Instruction::FDiv: |
| 4790 | case Instruction::FRem: |
| 4791 | case Instruction::Shl: |
| 4792 | case Instruction::LShr: |
| 4793 | case Instruction::AShr: |
| 4794 | case Instruction::And: |
| 4795 | case Instruction::Or: |
| 4796 | case Instruction::Xor: { |
| 4797 | // Just widen unops and binops. |
| 4798 | setDebugLocFromInst(Builder, &I); |
| 4799 | |
| 4800 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4801 | SmallVector<Value *, 2> Ops; |
| 4802 | for (VPValue *VPOp : User.operands()) |
| 4803 | Ops.push_back(State.get(VPOp, Part)); |
| 4804 | |
| 4805 | Value *V = Builder.CreateNAryOp(I.getOpcode(), Ops); |
| 4806 | |
| 4807 | if (auto *VecOp = dyn_cast<Instruction>(V)) |
| 4808 | VecOp->copyIRFlags(&I); |
| 4809 | |
| 4810 | // Use this vector value for all users of the original instruction. |
| 4811 | State.set(Def, &I, V, Part); |
| 4812 | addMetadata(V, &I); |
| 4813 | } |
| 4814 | |
| 4815 | break; |
| 4816 | } |
| 4817 | case Instruction::ICmp: |
| 4818 | case Instruction::FCmp: { |
| 4819 | // Widen compares. Generate vector compares. |
| 4820 | bool FCmp = (I.getOpcode() == Instruction::FCmp); |
| 4821 | auto *Cmp = cast<CmpInst>(&I); |
| 4822 | setDebugLocFromInst(Builder, Cmp); |
| 4823 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4824 | Value *A = State.get(User.getOperand(0), Part); |
| 4825 | Value *B = State.get(User.getOperand(1), Part); |
| 4826 | Value *C = nullptr; |
| 4827 | if (FCmp) { |
| 4828 | // Propagate fast math flags. |
| 4829 | IRBuilder<>::FastMathFlagGuard FMFG(Builder); |
| 4830 | Builder.setFastMathFlags(Cmp->getFastMathFlags()); |
| 4831 | C = Builder.CreateFCmp(Cmp->getPredicate(), A, B); |
| 4832 | } else { |
| 4833 | C = Builder.CreateICmp(Cmp->getPredicate(), A, B); |
| 4834 | } |
| 4835 | State.set(Def, &I, C, Part); |
| 4836 | addMetadata(C, &I); |
| 4837 | } |
| 4838 | |
| 4839 | break; |
| 4840 | } |
| 4841 | |
| 4842 | case Instruction::ZExt: |
| 4843 | case Instruction::SExt: |
| 4844 | case Instruction::FPToUI: |
| 4845 | case Instruction::FPToSI: |
| 4846 | case Instruction::FPExt: |
| 4847 | case Instruction::PtrToInt: |
| 4848 | case Instruction::IntToPtr: |
| 4849 | case Instruction::SIToFP: |
| 4850 | case Instruction::UIToFP: |
| 4851 | case Instruction::Trunc: |
| 4852 | case Instruction::FPTrunc: |
| 4853 | case Instruction::BitCast: { |
| 4854 | auto *CI = cast<CastInst>(&I); |
| 4855 | setDebugLocFromInst(Builder, CI); |
| 4856 | |
| 4857 | /// Vectorize casts. |
| 4858 | Type *DestTy = |
| 4859 | (VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF); |
| 4860 | |
| 4861 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4862 | Value *A = State.get(User.getOperand(0), Part); |
| 4863 | Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy); |
| 4864 | State.set(Def, &I, Cast, Part); |
| 4865 | addMetadata(Cast, &I); |
| 4866 | } |
| 4867 | break; |
| 4868 | } |
| 4869 | default: |
| 4870 | // This instruction is not vectorized by simple widening. |
| 4871 | LLVM_DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I); |
| 4872 | llvm_unreachable("Unhandled instruction!" ); |
| 4873 | } // end of switch. |
| 4874 | } |
| 4875 | |
| 4876 | void InnerLoopVectorizer::widenCallInstruction(CallInst &I, VPValue *Def, |
| 4877 | VPUser &ArgOperands, |
| 4878 | VPTransformState &State) { |
| 4879 | assert(!isa<DbgInfoIntrinsic>(I) && |
| 4880 | "DbgInfoIntrinsic should have been dropped during VPlan construction" ); |
| 4881 | setDebugLocFromInst(Builder, &I); |
| 4882 | |
| 4883 | Module *M = I.getParent()->getParent()->getParent(); |
| 4884 | auto *CI = cast<CallInst>(&I); |
| 4885 | |
| 4886 | SmallVector<Type *, 4> Tys; |
| 4887 | for (Value *ArgOperand : CI->arg_operands()) |
| 4888 | Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.getKnownMinValue())); |
| 4889 | |
| 4890 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 4891 | |
| 4892 | // The flag shows whether we use Intrinsic or a usual Call for vectorized |
| 4893 | // version of the instruction. |
| 4894 | // Is it beneficial to perform intrinsic call compared to lib call? |
| 4895 | bool NeedToScalarize = false; |
| 4896 | InstructionCost CallCost = Cost->getVectorCallCost(CI, VF, NeedToScalarize); |
| 4897 | InstructionCost IntrinsicCost = ID ? Cost->getVectorIntrinsicCost(CI, VF) : 0; |
| 4898 | bool UseVectorIntrinsic = ID && IntrinsicCost <= CallCost; |
| 4899 | assert((UseVectorIntrinsic || !NeedToScalarize) && |
| 4900 | "Instruction should be scalarized elsewhere." ); |
| 4901 | assert(IntrinsicCost.isValid() && CallCost.isValid() && |
| 4902 | "Cannot have invalid costs while widening" ); |
| 4903 | |
| 4904 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4905 | SmallVector<Value *, 4> Args; |
| 4906 | for (auto &I : enumerate(ArgOperands.operands())) { |
| 4907 | // Some intrinsics have a scalar argument - don't replace it with a |
| 4908 | // vector. |
| 4909 | Value *Arg; |
| 4910 | if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, I.index())) |
| 4911 | Arg = State.get(I.value(), Part); |
| 4912 | else |
| 4913 | Arg = State.get(I.value(), {0, 0}); |
| 4914 | Args.push_back(Arg); |
| 4915 | } |
| 4916 | |
| 4917 | Function *VectorF; |
| 4918 | if (UseVectorIntrinsic) { |
| 4919 | // Use vector version of the intrinsic. |
| 4920 | Type *TysForDecl[] = {CI->getType()}; |
| 4921 | if (VF.isVector()) { |
| 4922 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 4923 | TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF); |
| 4924 | } |
| 4925 | VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl); |
| 4926 | assert(VectorF && "Can't retrieve vector intrinsic." ); |
| 4927 | } else { |
| 4928 | // Use vector version of the function call. |
| 4929 | const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| 4930 | #ifndef NDEBUG |
| 4931 | assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr && |
| 4932 | "Can't create vector function." ); |
| 4933 | #endif |
| 4934 | VectorF = VFDatabase(*CI).getVectorizedFunction(Shape); |
| 4935 | } |
| 4936 | SmallVector<OperandBundleDef, 1> OpBundles; |
| 4937 | CI->getOperandBundlesAsDefs(OpBundles); |
| 4938 | CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles); |
| 4939 | |
| 4940 | if (isa<FPMathOperator>(V)) |
| 4941 | V->copyFastMathFlags(CI); |
| 4942 | |
| 4943 | State.set(Def, &I, V, Part); |
| 4944 | addMetadata(V, &I); |
| 4945 | } |
| 4946 | } |
| 4947 | |
| 4948 | void InnerLoopVectorizer::widenSelectInstruction(SelectInst &I, VPValue *VPDef, |
| 4949 | VPUser &Operands, |
| 4950 | bool InvariantCond, |
| 4951 | VPTransformState &State) { |
| 4952 | setDebugLocFromInst(Builder, &I); |
| 4953 | |
| 4954 | // The condition can be loop invariant but still defined inside the |
| 4955 | // loop. This means that we can't just use the original 'cond' value. |
| 4956 | // We have to take the 'vectorized' value and pick the first lane. |
| 4957 | // Instcombine will make this a no-op. |
| 4958 | auto *InvarCond = |
| 4959 | InvariantCond ? State.get(Operands.getOperand(0), {0, 0}) : nullptr; |
| 4960 | |
| 4961 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4962 | Value *Cond = |
| 4963 | InvarCond ? InvarCond : State.get(Operands.getOperand(0), Part); |
| 4964 | Value *Op0 = State.get(Operands.getOperand(1), Part); |
| 4965 | Value *Op1 = State.get(Operands.getOperand(2), Part); |
| 4966 | Value *Sel = Builder.CreateSelect(Cond, Op0, Op1); |
| 4967 | State.set(VPDef, &I, Sel, Part); |
| 4968 | addMetadata(Sel, &I); |
| 4969 | } |
| 4970 | } |
| 4971 | |
| 4972 | void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) { |
| 4973 | // We should not collect Scalars more than once per VF. Right now, this |
| 4974 | // function is called from collectUniformsAndScalars(), which already does |
| 4975 | // this check. Collecting Scalars for VF=1 does not make any sense. |
| 4976 | assert(VF.isVector() && Scalars.find(VF) == Scalars.end() && |
| 4977 | "This function should not be visited twice for the same VF" ); |
| 4978 | |
| 4979 | SmallSetVector<Instruction *, 8> Worklist; |
| 4980 | |
| 4981 | // These sets are used to seed the analysis with pointers used by memory |
| 4982 | // accesses that will remain scalar. |
| 4983 | SmallSetVector<Instruction *, 8> ScalarPtrs; |
| 4984 | SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs; |
| 4985 | auto *Latch = TheLoop->getLoopLatch(); |
| 4986 | |
| 4987 | // A helper that returns true if the use of Ptr by MemAccess will be scalar. |
| 4988 | // The pointer operands of loads and stores will be scalar as long as the |
| 4989 | // memory access is not a gather or scatter operation. The value operand of a |
| 4990 | // store will remain scalar if the store is scalarized. |
| 4991 | auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) { |
| 4992 | InstWidening WideningDecision = getWideningDecision(MemAccess, VF); |
| 4993 | assert(WideningDecision != CM_Unknown && |
| 4994 | "Widening decision should be ready at this moment" ); |
| 4995 | if (auto *Store = dyn_cast<StoreInst>(MemAccess)) |
| 4996 | if (Ptr == Store->getValueOperand()) |
| 4997 | return WideningDecision == CM_Scalarize; |
| 4998 | assert(Ptr == getLoadStorePointerOperand(MemAccess) && |
| 4999 | "Ptr is neither a value or pointer operand" ); |
| 5000 | return WideningDecision != CM_GatherScatter; |
| 5001 | }; |
| 5002 | |
| 5003 | // A helper that returns true if the given value is a bitcast or |
| 5004 | // getelementptr instruction contained in the loop. |
| 5005 | auto isLoopVaryingBitCastOrGEP = [&](Value *V) { |
| 5006 | return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) || |
| 5007 | isa<GetElementPtrInst>(V)) && |
| 5008 | !TheLoop->isLoopInvariant(V); |
| 5009 | }; |
| 5010 | |
| 5011 | auto isScalarPtrInduction = [&](Instruction *MemAccess, Value *Ptr) { |
| 5012 | if (!isa<PHINode>(Ptr) || |
| 5013 | !Legal->getInductionVars().count(cast<PHINode>(Ptr))) |
| 5014 | return false; |
| 5015 | auto &Induction = Legal->getInductionVars()[cast<PHINode>(Ptr)]; |
| 5016 | if (Induction.getKind() != InductionDescriptor::IK_PtrInduction) |
| 5017 | return false; |
| 5018 | return isScalarUse(MemAccess, Ptr); |
| 5019 | }; |
| 5020 | |
| 5021 | // A helper that evaluates a memory access's use of a pointer. If the |
| 5022 | // pointer is actually the pointer induction of a loop, it is being |
| 5023 | // inserted into Worklist. If the use will be a scalar use, and the |
| 5024 | // pointer is only used by memory accesses, we place the pointer in |
| 5025 | // ScalarPtrs. Otherwise, the pointer is placed in PossibleNonScalarPtrs. |
| 5026 | auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) { |
| 5027 | if (isScalarPtrInduction(MemAccess, Ptr)) { |
| 5028 | Worklist.insert(cast<Instruction>(Ptr)); |
| 5029 | Instruction *Update = cast<Instruction>( |
| 5030 | cast<PHINode>(Ptr)->getIncomingValueForBlock(Latch)); |
| 5031 | Worklist.insert(Update); |
| 5032 | LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Ptr |
| 5033 | << "\n" ); |
| 5034 | LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Update |
| 5035 | << "\n" ); |
| 5036 | return; |
| 5037 | } |
| 5038 | // We only care about bitcast and getelementptr instructions contained in |
| 5039 | // the loop. |
| 5040 | if (!isLoopVaryingBitCastOrGEP(Ptr)) |
| 5041 | return; |
| 5042 | |
| 5043 | // If the pointer has already been identified as scalar (e.g., if it was |
| 5044 | // also identified as uniform), there's nothing to do. |
| 5045 | auto *I = cast<Instruction>(Ptr); |
| 5046 | if (Worklist.count(I)) |
| 5047 | return; |
| 5048 | |
| 5049 | // If the use of the pointer will be a scalar use, and all users of the |
| 5050 | // pointer are memory accesses, place the pointer in ScalarPtrs. Otherwise, |
| 5051 | // place the pointer in PossibleNonScalarPtrs. |
| 5052 | if (isScalarUse(MemAccess, Ptr) && llvm::all_of(I->users(), [&](User *U) { |
| 5053 | return isa<LoadInst>(U) || isa<StoreInst>(U); |
| 5054 | })) |
| 5055 | ScalarPtrs.insert(I); |
| 5056 | else |
| 5057 | PossibleNonScalarPtrs.insert(I); |
| 5058 | }; |
| 5059 | |
| 5060 | // We seed the scalars analysis with three classes of instructions: (1) |
| 5061 | // instructions marked uniform-after-vectorization and (2) bitcast, |
| 5062 | // getelementptr and (pointer) phi instructions used by memory accesses |
| 5063 | // requiring a scalar use. |
| 5064 | // |
| 5065 | // (1) Add to the worklist all instructions that have been identified as |
| 5066 | // uniform-after-vectorization. |
| 5067 | Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end()); |
| 5068 | |
| 5069 | // (2) Add to the worklist all bitcast and getelementptr instructions used by |
| 5070 | // memory accesses requiring a scalar use. The pointer operands of loads and |
| 5071 | // stores will be scalar as long as the memory accesses is not a gather or |
| 5072 | // scatter operation. The value operand of a store will remain scalar if the |
| 5073 | // store is scalarized. |
| 5074 | for (auto *BB : TheLoop->blocks()) |
| 5075 | for (auto &I : *BB) { |
| 5076 | if (auto *Load = dyn_cast<LoadInst>(&I)) { |
| 5077 | evaluatePtrUse(Load, Load->getPointerOperand()); |
| 5078 | } else if (auto *Store = dyn_cast<StoreInst>(&I)) { |
| 5079 | evaluatePtrUse(Store, Store->getPointerOperand()); |
| 5080 | evaluatePtrUse(Store, Store->getValueOperand()); |
| 5081 | } |
| 5082 | } |
| 5083 | for (auto *I : ScalarPtrs) |
| 5084 | if (!PossibleNonScalarPtrs.count(I)) { |
| 5085 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n" ); |
| 5086 | Worklist.insert(I); |
| 5087 | } |
| 5088 | |
| 5089 | // Insert the forced scalars. |
| 5090 | // FIXME: Currently widenPHIInstruction() often creates a dead vector |
| 5091 | // induction variable when the PHI user is scalarized. |
| 5092 | auto ForcedScalar = ForcedScalars.find(VF); |
| 5093 | if (ForcedScalar != ForcedScalars.end()) |
| 5094 | for (auto *I : ForcedScalar->second) |
| 5095 | Worklist.insert(I); |
| 5096 | |
| 5097 | // Expand the worklist by looking through any bitcasts and getelementptr |
| 5098 | // instructions we've already identified as scalar. This is similar to the |
| 5099 | // expansion step in collectLoopUniforms(); however, here we're only |
| 5100 | // expanding to include additional bitcasts and getelementptr instructions. |
| 5101 | unsigned Idx = 0; |
| 5102 | while (Idx != Worklist.size()) { |
| 5103 | Instruction *Dst = Worklist[Idx++]; |
| 5104 | if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0))) |
| 5105 | continue; |
| 5106 | auto *Src = cast<Instruction>(Dst->getOperand(0)); |
| 5107 | if (llvm::all_of(Src->users(), [&](User *U) -> bool { |
| 5108 | auto *J = cast<Instruction>(U); |
| 5109 | return !TheLoop->contains(J) || Worklist.count(J) || |
| 5110 | ((isa<LoadInst>(J) || isa<StoreInst>(J)) && |
| 5111 | isScalarUse(J, Src)); |
| 5112 | })) { |
| 5113 | Worklist.insert(Src); |
| 5114 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n" ); |
| 5115 | } |
| 5116 | } |
| 5117 | |
| 5118 | // An induction variable will remain scalar if all users of the induction |
| 5119 | // variable and induction variable update remain scalar. |
| 5120 | for (auto &Induction : Legal->getInductionVars()) { |
| 5121 | auto *Ind = Induction.first; |
| 5122 | auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| 5123 | |
| 5124 | // If tail-folding is applied, the primary induction variable will be used |
| 5125 | // to feed a vector compare. |
| 5126 | if (Ind == Legal->getPrimaryInduction() && foldTailByMasking()) |
| 5127 | continue; |
| 5128 | |
| 5129 | // Determine if all users of the induction variable are scalar after |
| 5130 | // vectorization. |
| 5131 | auto ScalarInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { |
| 5132 | auto *I = cast<Instruction>(U); |
| 5133 | return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I); |
| 5134 | }); |
| 5135 | if (!ScalarInd) |
| 5136 | continue; |
| 5137 | |
| 5138 | // Determine if all users of the induction variable update instruction are |
| 5139 | // scalar after vectorization. |
| 5140 | auto ScalarIndUpdate = |
| 5141 | llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| 5142 | auto *I = cast<Instruction>(U); |
| 5143 | return I == Ind || !TheLoop->contains(I) || Worklist.count(I); |
| 5144 | }); |
| 5145 | if (!ScalarIndUpdate) |
| 5146 | continue; |
| 5147 | |
| 5148 | // The induction variable and its update instruction will remain scalar. |
| 5149 | Worklist.insert(Ind); |
| 5150 | Worklist.insert(IndUpdate); |
| 5151 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n" ); |
| 5152 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate |
| 5153 | << "\n" ); |
| 5154 | } |
| 5155 | |
| 5156 | Scalars[VF].insert(Worklist.begin(), Worklist.end()); |
| 5157 | } |
| 5158 | |
| 5159 | bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I, |
| 5160 | ElementCount VF) { |
| 5161 | if (!blockNeedsPredication(I->getParent())) |
| 5162 | return false; |
| 5163 | switch(I->getOpcode()) { |
| 5164 | default: |
| 5165 | break; |
| 5166 | case Instruction::Load: |
| 5167 | case Instruction::Store: { |
| 5168 | if (!Legal->isMaskRequired(I)) |
| 5169 | return false; |
| 5170 | auto *Ptr = getLoadStorePointerOperand(I); |
| 5171 | auto *Ty = getMemInstValueType(I); |
| 5172 | // We have already decided how to vectorize this instruction, get that |
| 5173 | // result. |
| 5174 | if (VF.isVector()) { |
| 5175 | InstWidening WideningDecision = getWideningDecision(I, VF); |
| 5176 | assert(WideningDecision != CM_Unknown && |
| 5177 | "Widening decision should be ready at this moment" ); |
| 5178 | return WideningDecision == CM_Scalarize; |
| 5179 | } |
| 5180 | const Align Alignment = getLoadStoreAlignment(I); |
| 5181 | return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) || |
| 5182 | isLegalMaskedGather(Ty, Alignment)) |
| 5183 | : !(isLegalMaskedStore(Ty, Ptr, Alignment) || |
| 5184 | isLegalMaskedScatter(Ty, Alignment)); |
| 5185 | } |
| 5186 | case Instruction::UDiv: |
| 5187 | case Instruction::SDiv: |
| 5188 | case Instruction::SRem: |
| 5189 | case Instruction::URem: |
| 5190 | return mayDivideByZero(*I); |
| 5191 | } |
| 5192 | return false; |
| 5193 | } |
| 5194 | |
| 5195 | bool LoopVectorizationCostModel::interleavedAccessCanBeWidened( |
| 5196 | Instruction *I, ElementCount VF) { |
| 5197 | assert(isAccessInterleaved(I) && "Expecting interleaved access." ); |
| 5198 | assert(getWideningDecision(I, VF) == CM_Unknown && |
| 5199 | "Decision should not be set yet." ); |
| 5200 | auto *Group = getInterleavedAccessGroup(I); |
| 5201 | assert(Group && "Must have a group." ); |
| 5202 | |
| 5203 | // If the instruction's allocated size doesn't equal it's type size, it |
| 5204 | // requires padding and will be scalarized. |
| 5205 | auto &DL = I->getModule()->getDataLayout(); |
| 5206 | auto *ScalarTy = getMemInstValueType(I); |
| 5207 | if (hasIrregularType(ScalarTy, DL)) |
| 5208 | return false; |
| 5209 | |
| 5210 | // Check if masking is required. |
| 5211 | // A Group may need masking for one of two reasons: it resides in a block that |
| 5212 | // needs predication, or it was decided to use masking to deal with gaps. |
| 5213 | bool PredicatedAccessRequiresMasking = |
| 5214 | Legal->blockNeedsPredication(I->getParent()) && Legal->isMaskRequired(I); |
| 5215 | bool AccessWithGapsRequiresMasking = |
| 5216 | Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed(); |
| 5217 | if (!PredicatedAccessRequiresMasking && !AccessWithGapsRequiresMasking) |
| 5218 | return true; |
| 5219 | |
| 5220 | // If masked interleaving is required, we expect that the user/target had |
| 5221 | // enabled it, because otherwise it either wouldn't have been created or |
| 5222 | // it should have been invalidated by the CostModel. |
| 5223 | assert(useMaskedInterleavedAccesses(TTI) && |
| 5224 | "Masked interleave-groups for predicated accesses are not enabled." ); |
| 5225 | |
| 5226 | auto *Ty = getMemInstValueType(I); |
| 5227 | const Align Alignment = getLoadStoreAlignment(I); |
| 5228 | return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment) |
| 5229 | : TTI.isLegalMaskedStore(Ty, Alignment); |
| 5230 | } |
| 5231 | |
| 5232 | bool LoopVectorizationCostModel::memoryInstructionCanBeWidened( |
| 5233 | Instruction *I, ElementCount VF) { |
| 5234 | // Get and ensure we have a valid memory instruction. |
| 5235 | LoadInst *LI = dyn_cast<LoadInst>(I); |
| 5236 | StoreInst *SI = dyn_cast<StoreInst>(I); |
| 5237 | assert((LI || SI) && "Invalid memory instruction" ); |
| 5238 | |
| 5239 | auto *Ptr = getLoadStorePointerOperand(I); |
| 5240 | |
| 5241 | // In order to be widened, the pointer should be consecutive, first of all. |
| 5242 | if (!Legal->isConsecutivePtr(Ptr)) |
| 5243 | return false; |
| 5244 | |
| 5245 | // If the instruction is a store located in a predicated block, it will be |
| 5246 | // scalarized. |
| 5247 | if (isScalarWithPredication(I)) |
| 5248 | return false; |
| 5249 | |
| 5250 | // If the instruction's allocated size doesn't equal it's type size, it |
| 5251 | // requires padding and will be scalarized. |
| 5252 | auto &DL = I->getModule()->getDataLayout(); |
| 5253 | auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType(); |
| 5254 | if (hasIrregularType(ScalarTy, DL)) |
| 5255 | return false; |
| 5256 | |
| 5257 | return true; |
| 5258 | } |
| 5259 | |
| 5260 | void LoopVectorizationCostModel::collectLoopUniforms(ElementCount VF) { |
| 5261 | // We should not collect Uniforms more than once per VF. Right now, |
| 5262 | // this function is called from collectUniformsAndScalars(), which |
| 5263 | // already does this check. Collecting Uniforms for VF=1 does not make any |
| 5264 | // sense. |
| 5265 | |
| 5266 | assert(VF.isVector() && Uniforms.find(VF) == Uniforms.end() && |
| 5267 | "This function should not be visited twice for the same VF" ); |
| 5268 | |
| 5269 | // Visit the list of Uniforms. If we'll not find any uniform value, we'll |
| 5270 | // not analyze again. Uniforms.count(VF) will return 1. |
| 5271 | Uniforms[VF].clear(); |
| 5272 | |
| 5273 | // We now know that the loop is vectorizable! |
| 5274 | // Collect instructions inside the loop that will remain uniform after |
| 5275 | // vectorization. |
| 5276 | |
| 5277 | // Global values, params and instructions outside of current loop are out of |
| 5278 | // scope. |
| 5279 | auto isOutOfScope = [&](Value *V) -> bool { |
| 5280 | Instruction *I = dyn_cast<Instruction>(V); |
| 5281 | return (!I || !TheLoop->contains(I)); |
| 5282 | }; |
| 5283 | |
| 5284 | SetVector<Instruction *> Worklist; |
| 5285 | BasicBlock *Latch = TheLoop->getLoopLatch(); |
| 5286 | |
| 5287 | // Instructions that are scalar with predication must not be considered |
| 5288 | // uniform after vectorization, because that would create an erroneous |
| 5289 | // replicating region where only a single instance out of VF should be formed. |
| 5290 | // TODO: optimize such seldom cases if found important, see PR40816. |
| 5291 | auto addToWorklistIfAllowed = [&](Instruction *I) -> void { |
| 5292 | if (isOutOfScope(I)) { |
| 5293 | LLVM_DEBUG(dbgs() << "LV: Found not uniform due to scope: " |
| 5294 | << *I << "\n" ); |
| 5295 | return; |
| 5296 | } |
| 5297 | if (isScalarWithPredication(I, VF)) { |
| 5298 | LLVM_DEBUG(dbgs() << "LV: Found not uniform being ScalarWithPredication: " |
| 5299 | << *I << "\n" ); |
| 5300 | return; |
| 5301 | } |
| 5302 | LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n" ); |
| 5303 | Worklist.insert(I); |
| 5304 | }; |
| 5305 | |
| 5306 | // Start with the conditional branch. If the branch condition is an |
| 5307 | // instruction contained in the loop that is only used by the branch, it is |
| 5308 | // uniform. |
| 5309 | auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0)); |
| 5310 | if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse()) |
| 5311 | addToWorklistIfAllowed(Cmp); |
| 5312 | |
| 5313 | auto isUniformDecision = [&](Instruction *I, ElementCount VF) { |
| 5314 | InstWidening WideningDecision = getWideningDecision(I, VF); |
| 5315 | assert(WideningDecision != CM_Unknown && |
| 5316 | "Widening decision should be ready at this moment" ); |
| 5317 | |
| 5318 | // A uniform memory op is itself uniform. We exclude uniform stores |
| 5319 | // here as they demand the last lane, not the first one. |
| 5320 | if (isa<LoadInst>(I) && Legal->isUniformMemOp(*I)) { |
| 5321 | assert(WideningDecision == CM_Scalarize); |
| 5322 | return true; |
| 5323 | } |
| 5324 | |
| 5325 | return (WideningDecision == CM_Widen || |
| 5326 | WideningDecision == CM_Widen_Reverse || |
| 5327 | WideningDecision == CM_Interleave); |
| 5328 | }; |
| 5329 | |
| 5330 | |
| 5331 | // Returns true if Ptr is the pointer operand of a memory access instruction |
| 5332 | // I, and I is known to not require scalarization. |
| 5333 | auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool { |
| 5334 | return getLoadStorePointerOperand(I) == Ptr && isUniformDecision(I, VF); |
| 5335 | }; |
| 5336 | |
| 5337 | // Holds a list of values which are known to have at least one uniform use. |
| 5338 | // Note that there may be other uses which aren't uniform. A "uniform use" |
| 5339 | // here is something which only demands lane 0 of the unrolled iterations; |
| 5340 | // it does not imply that all lanes produce the same value (e.g. this is not |
| 5341 | // the usual meaning of uniform) |
| 5342 | SmallPtrSet<Value *, 8> HasUniformUse; |
| 5343 | |
| 5344 | // Scan the loop for instructions which are either a) known to have only |
| 5345 | // lane 0 demanded or b) are uses which demand only lane 0 of their operand. |
| 5346 | for (auto *BB : TheLoop->blocks()) |
| 5347 | for (auto &I : *BB) { |
| 5348 | // If there's no pointer operand, there's nothing to do. |
| 5349 | auto *Ptr = getLoadStorePointerOperand(&I); |
| 5350 | if (!Ptr) |
| 5351 | continue; |
| 5352 | |
| 5353 | // A uniform memory op is itself uniform. We exclude uniform stores |
| 5354 | // here as they demand the last lane, not the first one. |
| 5355 | if (isa<LoadInst>(I) && Legal->isUniformMemOp(I)) |
| 5356 | addToWorklistIfAllowed(&I); |
| 5357 | |
| 5358 | if (isUniformDecision(&I, VF)) { |
| 5359 | assert(isVectorizedMemAccessUse(&I, Ptr) && "consistency check" ); |
| 5360 | HasUniformUse.insert(Ptr); |
| 5361 | } |
| 5362 | } |
| 5363 | |
| 5364 | // Add to the worklist any operands which have *only* uniform (e.g. lane 0 |
| 5365 | // demanding) users. Since loops are assumed to be in LCSSA form, this |
| 5366 | // disallows uses outside the loop as well. |
| 5367 | for (auto *V : HasUniformUse) { |
| 5368 | if (isOutOfScope(V)) |
| 5369 | continue; |
| 5370 | auto *I = cast<Instruction>(V); |
| 5371 | auto UsersAreMemAccesses = |
| 5372 | llvm::all_of(I->users(), [&](User *U) -> bool { |
| 5373 | return isVectorizedMemAccessUse(cast<Instruction>(U), V); |
| 5374 | }); |
| 5375 | if (UsersAreMemAccesses) |
| 5376 | addToWorklistIfAllowed(I); |
| 5377 | } |
| 5378 | |
| 5379 | // Expand Worklist in topological order: whenever a new instruction |
| 5380 | // is added , its users should be already inside Worklist. It ensures |
| 5381 | // a uniform instruction will only be used by uniform instructions. |
| 5382 | unsigned idx = 0; |
| 5383 | while (idx != Worklist.size()) { |
| 5384 | Instruction *I = Worklist[idx++]; |
| 5385 | |
| 5386 | for (auto OV : I->operand_values()) { |
| 5387 | // isOutOfScope operands cannot be uniform instructions. |
| 5388 | if (isOutOfScope(OV)) |
| 5389 | continue; |
| 5390 | // First order recurrence Phi's should typically be considered |
| 5391 | // non-uniform. |
| 5392 | auto *OP = dyn_cast<PHINode>(OV); |
| 5393 | if (OP && Legal->isFirstOrderRecurrence(OP)) |
| 5394 | continue; |
| 5395 | // If all the users of the operand are uniform, then add the |
| 5396 | // operand into the uniform worklist. |
| 5397 | auto *OI = cast<Instruction>(OV); |
| 5398 | if (llvm::all_of(OI->users(), [&](User *U) -> bool { |
| 5399 | auto *J = cast<Instruction>(U); |
| 5400 | return Worklist.count(J) || isVectorizedMemAccessUse(J, OI); |
| 5401 | })) |
| 5402 | addToWorklistIfAllowed(OI); |
| 5403 | } |
| 5404 | } |
| 5405 | |
| 5406 | // For an instruction to be added into Worklist above, all its users inside |
| 5407 | // the loop should also be in Worklist. However, this condition cannot be |
| 5408 | // true for phi nodes that form a cyclic dependence. We must process phi |
| 5409 | // nodes separately. An induction variable will remain uniform if all users |
| 5410 | // of the induction variable and induction variable update remain uniform. |
| 5411 | // The code below handles both pointer and non-pointer induction variables. |
| 5412 | for (auto &Induction : Legal->getInductionVars()) { |
| 5413 | auto *Ind = Induction.first; |
| 5414 | auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| 5415 | |
| 5416 | // Determine if all users of the induction variable are uniform after |
| 5417 | // vectorization. |
| 5418 | auto UniformInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { |
| 5419 | auto *I = cast<Instruction>(U); |
| 5420 | return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) || |
| 5421 | isVectorizedMemAccessUse(I, Ind); |
| 5422 | }); |
| 5423 | if (!UniformInd) |
| 5424 | continue; |
| 5425 | |
| 5426 | // Determine if all users of the induction variable update instruction are |
| 5427 | // uniform after vectorization. |
| 5428 | auto UniformIndUpdate = |
| 5429 | llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| 5430 | auto *I = cast<Instruction>(U); |
| 5431 | return I == Ind || !TheLoop->contains(I) || Worklist.count(I) || |
| 5432 | isVectorizedMemAccessUse(I, IndUpdate); |
| 5433 | }); |
| 5434 | if (!UniformIndUpdate) |
| 5435 | continue; |
| 5436 | |
| 5437 | // The induction variable and its update instruction will remain uniform. |
| 5438 | addToWorklistIfAllowed(Ind); |
| 5439 | addToWorklistIfAllowed(IndUpdate); |
| 5440 | } |
| 5441 | |
| 5442 | Uniforms[VF].insert(Worklist.begin(), Worklist.end()); |
| 5443 | } |
| 5444 | |
| 5445 | bool LoopVectorizationCostModel::runtimeChecksRequired() { |
| 5446 | LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n" ); |
| 5447 | |
| 5448 | if (Legal->getRuntimePointerChecking()->Need) { |
| 5449 | reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz" , |
| 5450 | "runtime pointer checks needed. Enable vectorization of this " |
| 5451 | "loop with '#pragma clang loop vectorize(enable)' when " |
| 5452 | "compiling with -Os/-Oz" , |
| 5453 | "CantVersionLoopWithOptForSize" , ORE, TheLoop); |
| 5454 | return true; |
| 5455 | } |
| 5456 | |
| 5457 | if (!PSE.getUnionPredicate().getPredicates().empty()) { |
| 5458 | reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz" , |
| 5459 | "runtime SCEV checks needed. Enable vectorization of this " |
| 5460 | "loop with '#pragma clang loop vectorize(enable)' when " |
| 5461 | "compiling with -Os/-Oz" , |
| 5462 | "CantVersionLoopWithOptForSize" , ORE, TheLoop); |
| 5463 | return true; |
| 5464 | } |
| 5465 | |
| 5466 | // FIXME: Avoid specializing for stride==1 instead of bailing out. |
| 5467 | if (!Legal->getLAI()->getSymbolicStrides().empty()) { |
| 5468 | reportVectorizationFailure("Runtime stride check for small trip count" , |
| 5469 | "runtime stride == 1 checks needed. Enable vectorization of " |
| 5470 | "this loop without such check by compiling with -Os/-Oz" , |
| 5471 | "CantVersionLoopWithOptForSize" , ORE, TheLoop); |
| 5472 | return true; |
| 5473 | } |
| 5474 | |
| 5475 | return false; |
| 5476 | } |
| 5477 | |
| 5478 | Optional<ElementCount> |
| 5479 | LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) { |
| 5480 | if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) { |
| 5481 | // TODO: It may by useful to do since it's still likely to be dynamically |
| 5482 | // uniform if the target can skip. |
| 5483 | reportVectorizationFailure( |
| 5484 | "Not inserting runtime ptr check for divergent target" , |
| 5485 | "runtime pointer checks needed. Not enabled for divergent target" , |
| 5486 | "CantVersionLoopWithDivergentTarget" , ORE, TheLoop); |
| 5487 | return None; |
| 5488 | } |
| 5489 | |
| 5490 | unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop); |
| 5491 | LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n'); |
| 5492 | if (TC == 1) { |
| 5493 | reportVectorizationFailure("Single iteration (non) loop" , |
| 5494 | "loop trip count is one, irrelevant for vectorization" , |
| 5495 | "SingleIterationLoop" , ORE, TheLoop); |
| 5496 | return None; |
| 5497 | } |
| 5498 | |
| 5499 | switch (ScalarEpilogueStatus) { |
| 5500 | case CM_ScalarEpilogueAllowed: |
| 5501 | return computeFeasibleMaxVF(TC, UserVF); |
| 5502 | case CM_ScalarEpilogueNotAllowedUsePredicate: |
| 5503 | LLVM_FALLTHROUGH; |
| 5504 | case CM_ScalarEpilogueNotNeededUsePredicate: |
| 5505 | LLVM_DEBUG( |
| 5506 | dbgs() << "LV: vector predicate hint/switch found.\n" |
| 5507 | << "LV: Not allowing scalar epilogue, creating predicated " |
| 5508 | << "vector loop.\n" ); |
| 5509 | break; |
| 5510 | case CM_ScalarEpilogueNotAllowedLowTripLoop: |
| 5511 | // fallthrough as a special case of OptForSize |
| 5512 | case CM_ScalarEpilogueNotAllowedOptSize: |
| 5513 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedOptSize) |
| 5514 | LLVM_DEBUG( |
| 5515 | dbgs() << "LV: Not allowing scalar epilogue due to -Os/-Oz.\n" ); |
| 5516 | else |
| 5517 | LLVM_DEBUG(dbgs() << "LV: Not allowing scalar epilogue due to low trip " |
| 5518 | << "count.\n" ); |
| 5519 | |
| 5520 | // Bail if runtime checks are required, which are not good when optimising |
| 5521 | // for size. |
| 5522 | if (runtimeChecksRequired()) |
| 5523 | return None; |
| 5524 | |
| 5525 | break; |
| 5526 | } |
| 5527 | |
| 5528 | // The only loops we can vectorize without a scalar epilogue, are loops with |
| 5529 | // a bottom-test and a single exiting block. We'd have to handle the fact |
| 5530 | // that not every instruction executes on the last iteration. This will |
| 5531 | // require a lane mask which varies through the vector loop body. (TODO) |
| 5532 | if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) { |
| 5533 | // If there was a tail-folding hint/switch, but we can't fold the tail by |
| 5534 | // masking, fallback to a vectorization with a scalar epilogue. |
| 5535 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) { |
| 5536 | LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a " |
| 5537 | "scalar epilogue instead.\n" ); |
| 5538 | ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 5539 | return computeFeasibleMaxVF(TC, UserVF); |
| 5540 | } |
| 5541 | return None; |
| 5542 | } |
| 5543 | |
| 5544 | // Now try the tail folding |
| 5545 | |
| 5546 | // Invalidate interleave groups that require an epilogue if we can't mask |
| 5547 | // the interleave-group. |
| 5548 | if (!useMaskedInterleavedAccesses(TTI)) { |
| 5549 | assert(WideningDecisions.empty() && Uniforms.empty() && Scalars.empty() && |
| 5550 | "No decisions should have been taken at this point" ); |
| 5551 | // Note: There is no need to invalidate any cost modeling decisions here, as |
| 5552 | // non where taken so far. |
| 5553 | InterleaveInfo.invalidateGroupsRequiringScalarEpilogue(); |
| 5554 | } |
| 5555 | |
| 5556 | ElementCount MaxVF = computeFeasibleMaxVF(TC, UserVF); |
| 5557 | assert(!MaxVF.isScalable() && |
| 5558 | "Scalable vectors do not yet support tail folding" ); |
| 5559 | assert((UserVF.isNonZero() || isPowerOf2_32(MaxVF.getFixedValue())) && |
| 5560 | "MaxVF must be a power of 2" ); |
| 5561 | unsigned MaxVFtimesIC = |
| 5562 | UserIC ? MaxVF.getFixedValue() * UserIC : MaxVF.getFixedValue(); |
| 5563 | // Avoid tail folding if the trip count is known to be a multiple of any VF we |
| 5564 | // chose. |
| 5565 | ScalarEvolution *SE = PSE.getSE(); |
| 5566 | const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); |
| 5567 | const SCEV *ExitCount = SE->getAddExpr( |
| 5568 | BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); |
| 5569 | const SCEV *Rem = SE->getURemExpr( |
| 5570 | ExitCount, SE->getConstant(BackedgeTakenCount->getType(), MaxVFtimesIC)); |
| 5571 | if (Rem->isZero()) { |
| 5572 | // Accept MaxVF if we do not have a tail. |
| 5573 | LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n" ); |
| 5574 | return MaxVF; |
| 5575 | } |
| 5576 | |
| 5577 | // If we don't know the precise trip count, or if the trip count that we |
| 5578 | // found modulo the vectorization factor is not zero, try to fold the tail |
| 5579 | // by masking. |
| 5580 | // FIXME: look for a smaller MaxVF that does divide TC rather than masking. |
| 5581 | if (Legal->prepareToFoldTailByMasking()) { |
| 5582 | FoldTailByMasking = true; |
| 5583 | return MaxVF; |
| 5584 | } |
| 5585 | |
| 5586 | // If there was a tail-folding hint/switch, but we can't fold the tail by |
| 5587 | // masking, fallback to a vectorization with a scalar epilogue. |
| 5588 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) { |
| 5589 | LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a " |
| 5590 | "scalar epilogue instead.\n" ); |
| 5591 | ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 5592 | return MaxVF; |
| 5593 | } |
| 5594 | |
| 5595 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedUsePredicate) { |
| 5596 | LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n" ); |
| 5597 | return None; |
| 5598 | } |
| 5599 | |
| 5600 | if (TC == 0) { |
| 5601 | reportVectorizationFailure( |
| 5602 | "Unable to calculate the loop count due to complex control flow" , |
| 5603 | "unable to calculate the loop count due to complex control flow" , |
| 5604 | "UnknownLoopCountComplexCFG" , ORE, TheLoop); |
| 5605 | return None; |
| 5606 | } |
| 5607 | |
| 5608 | reportVectorizationFailure( |
| 5609 | "Cannot optimize for size and vectorize at the same time." , |
| 5610 | "cannot optimize for size and vectorize at the same time. " |
| 5611 | "Enable vectorization of this loop with '#pragma clang loop " |
| 5612 | "vectorize(enable)' when compiling with -Os/-Oz" , |
| 5613 | "NoTailLoopWithOptForSize" , ORE, TheLoop); |
| 5614 | return None; |
| 5615 | } |
| 5616 | |
| 5617 | ElementCount |
| 5618 | LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount, |
| 5619 | ElementCount UserVF) { |
| 5620 | bool IgnoreScalableUserVF = UserVF.isScalable() && |
| 5621 | !TTI.supportsScalableVectors() && |
| 5622 | !ForceTargetSupportsScalableVectors; |
| 5623 | if (IgnoreScalableUserVF) { |
| 5624 | LLVM_DEBUG( |
| 5625 | dbgs() << "LV: Ignoring VF=" << UserVF |
| 5626 | << " because target does not support scalable vectors.\n" ); |
| 5627 | ORE->emit([&]() { |
| 5628 | return OptimizationRemarkAnalysis(DEBUG_TYPE, "IgnoreScalableUserVF" , |
| 5629 | TheLoop->getStartLoc(), |
| 5630 | TheLoop->getHeader()) |
| 5631 | << "Ignoring VF=" << ore::NV("UserVF" , UserVF) |
| 5632 | << " because target does not support scalable vectors." ; |
| 5633 | }); |
| 5634 | } |
| 5635 | |
| 5636 | // Beyond this point two scenarios are handled. If UserVF isn't specified |
| 5637 | // then a suitable VF is chosen. If UserVF is specified and there are |
| 5638 | // dependencies, check if it's legal. However, if a UserVF is specified and |
| 5639 | // there are no dependencies, then there's nothing to do. |
| 5640 | if (UserVF.isNonZero() && !IgnoreScalableUserVF && |
| 5641 | Legal->isSafeForAnyVectorWidth()) |
| 5642 | return UserVF; |
| 5643 | |
| 5644 | MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI); |
| 5645 | unsigned SmallestType, WidestType; |
| 5646 | std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes(); |
| 5647 | unsigned WidestRegister = TTI.getRegisterBitWidth(true); |
| 5648 | |
| 5649 | // Get the maximum safe dependence distance in bits computed by LAA. |
| 5650 | // It is computed by MaxVF * sizeOf(type) * 8, where type is taken from |
| 5651 | // the memory accesses that is most restrictive (involved in the smallest |
| 5652 | // dependence distance). |
| 5653 | unsigned MaxSafeVectorWidthInBits = Legal->getMaxSafeVectorWidthInBits(); |
| 5654 | |
| 5655 | // If the user vectorization factor is legally unsafe, clamp it to a safe |
| 5656 | // value. Otherwise, return as is. |
| 5657 | if (UserVF.isNonZero() && !IgnoreScalableUserVF) { |
| 5658 | unsigned MaxSafeElements = |
| 5659 | PowerOf2Floor(MaxSafeVectorWidthInBits / WidestType); |
| 5660 | ElementCount MaxSafeVF = ElementCount::getFixed(MaxSafeElements); |
| 5661 | |
| 5662 | if (UserVF.isScalable()) { |
| 5663 | Optional<unsigned> MaxVScale = TTI.getMaxVScale(); |
| 5664 | |
| 5665 | // Scale VF by vscale before checking if it's safe. |
| 5666 | MaxSafeVF = ElementCount::getScalable( |
| 5667 | MaxVScale ? (MaxSafeElements / MaxVScale.getValue()) : 0); |
| 5668 | |
| 5669 | if (MaxSafeVF.isZero()) { |
| 5670 | // The dependence distance is too small to use scalable vectors, |
| 5671 | // fallback on fixed. |
| 5672 | LLVM_DEBUG( |
| 5673 | dbgs() |
| 5674 | << "LV: Max legal vector width too small, scalable vectorization " |
| 5675 | "unfeasible. Using fixed-width vectorization instead.\n" ); |
| 5676 | ORE->emit([&]() { |
| 5677 | return OptimizationRemarkAnalysis(DEBUG_TYPE, "ScalableVFUnfeasible" , |
| 5678 | TheLoop->getStartLoc(), |
| 5679 | TheLoop->getHeader()) |
| 5680 | << "Max legal vector width too small, scalable vectorization " |
| 5681 | << "unfeasible. Using fixed-width vectorization instead." ; |
| 5682 | }); |
| 5683 | return computeFeasibleMaxVF( |
| 5684 | ConstTripCount, ElementCount::getFixed(UserVF.getKnownMinValue())); |
| 5685 | } |
| 5686 | } |
| 5687 | |
| 5688 | LLVM_DEBUG(dbgs() << "LV: The max safe VF is: " << MaxSafeVF << ".\n" ); |
| 5689 | |
| 5690 | if (ElementCount::isKnownLE(UserVF, MaxSafeVF)) |
| 5691 | return UserVF; |
| 5692 | |
| 5693 | LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVF |
| 5694 | << " is unsafe, clamping to max safe VF=" << MaxSafeVF |
| 5695 | << ".\n" ); |
| 5696 | ORE->emit([&]() { |
| 5697 | return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationFactor" , |
| 5698 | TheLoop->getStartLoc(), |
| 5699 | TheLoop->getHeader()) |
| 5700 | << "User-specified vectorization factor " |
| 5701 | << ore::NV("UserVectorizationFactor" , UserVF) |
| 5702 | << " is unsafe, clamping to maximum safe vectorization factor " |
| 5703 | << ore::NV("VectorizationFactor" , MaxSafeVF); |
| 5704 | }); |
| 5705 | return MaxSafeVF; |
| 5706 | } |
| 5707 | |
| 5708 | WidestRegister = std::min(WidestRegister, MaxSafeVectorWidthInBits); |
| 5709 | |
| 5710 | // Ensure MaxVF is a power of 2; the dependence distance bound may not be. |
| 5711 | // Note that both WidestRegister and WidestType may not be a powers of 2. |
| 5712 | unsigned MaxVectorSize = PowerOf2Floor(WidestRegister / WidestType); |
| 5713 | |
| 5714 | LLVM_DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType |
| 5715 | << " / " << WidestType << " bits.\n" ); |
| 5716 | LLVM_DEBUG(dbgs() << "LV: The Widest register safe to use is: " |
| 5717 | << WidestRegister << " bits.\n" ); |
| 5718 | |
| 5719 | assert(MaxVectorSize <= WidestRegister && |
| 5720 | "Did not expect to pack so many elements" |
| 5721 | " into one vector!" ); |
| 5722 | if (MaxVectorSize == 0) { |
| 5723 | LLVM_DEBUG(dbgs() << "LV: The target has no vector registers.\n" ); |
| 5724 | MaxVectorSize = 1; |
| 5725 | return ElementCount::getFixed(MaxVectorSize); |
| 5726 | } else if (ConstTripCount && ConstTripCount < MaxVectorSize && |
| 5727 | isPowerOf2_32(ConstTripCount)) { |
| 5728 | // We need to clamp the VF to be the ConstTripCount. There is no point in |
| 5729 | // choosing a higher viable VF as done in the loop below. |
| 5730 | LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to the constant trip count: " |
| 5731 | << ConstTripCount << "\n" ); |
| 5732 | MaxVectorSize = ConstTripCount; |
| 5733 | return ElementCount::getFixed(MaxVectorSize); |
| 5734 | } |
| 5735 | |
| 5736 | unsigned MaxVF = MaxVectorSize; |
| 5737 | if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) || |
| 5738 | (MaximizeBandwidth && isScalarEpilogueAllowed())) { |
| 5739 | // Collect all viable vectorization factors larger than the default MaxVF |
| 5740 | // (i.e. MaxVectorSize). |
| 5741 | SmallVector<ElementCount, 8> VFs; |
| 5742 | unsigned NewMaxVectorSize = WidestRegister / SmallestType; |
| 5743 | for (unsigned VS = MaxVectorSize * 2; VS <= NewMaxVectorSize; VS *= 2) |
| 5744 | VFs.push_back(ElementCount::getFixed(VS)); |
| 5745 | |
| 5746 | // For each VF calculate its register usage. |
| 5747 | auto RUs = calculateRegisterUsage(VFs); |
| 5748 | |
| 5749 | // Select the largest VF which doesn't require more registers than existing |
| 5750 | // ones. |
| 5751 | for (int i = RUs.size() - 1; i >= 0; --i) { |
| 5752 | bool Selected = true; |
| 5753 | for (auto& pair : RUs[i].MaxLocalUsers) { |
| 5754 | unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); |
| 5755 | if (pair.second > TargetNumRegisters) |
| 5756 | Selected = false; |
| 5757 | } |
| 5758 | if (Selected) { |
| 5759 | MaxVF = VFs[i].getKnownMinValue(); |
| 5760 | break; |
| 5761 | } |
| 5762 | } |
| 5763 | if (unsigned MinVF = TTI.getMinimumVF(SmallestType)) { |
| 5764 | if (MaxVF < MinVF) { |
| 5765 | LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF |
| 5766 | << ") with target's minimum: " << MinVF << '\n'); |
| 5767 | MaxVF = MinVF; |
| 5768 | } |
| 5769 | } |
| 5770 | } |
| 5771 | return ElementCount::getFixed(MaxVF); |
| 5772 | } |
| 5773 | |
| 5774 | VectorizationFactor |
| 5775 | LoopVectorizationCostModel::selectVectorizationFactor(ElementCount MaxVF) { |
| 5776 | // FIXME: This can be fixed for scalable vectors later, because at this stage |
| 5777 | // the LoopVectorizer will only consider vectorizing a loop with scalable |
| 5778 | // vectors when the loop has a hint to enable vectorization for a given VF. |
| 5779 | assert(!MaxVF.isScalable() && "scalable vectors not yet supported" ); |
| 5780 | |
| 5781 | InstructionCost ExpectedCost = expectedCost(ElementCount::getFixed(1)).first; |
| 5782 | LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << ExpectedCost << ".\n" ); |
| 5783 | assert(ExpectedCost.isValid() && "Unexpected invalid cost for scalar loop" ); |
| 5784 | |
| 5785 | unsigned Width = 1; |
| 5786 | const float ScalarCost = *ExpectedCost.getValue(); |
| 5787 | float Cost = ScalarCost; |
| 5788 | |
| 5789 | bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled; |
| 5790 | if (ForceVectorization && MaxVF.isVector()) { |
| 5791 | // Ignore scalar width, because the user explicitly wants vectorization. |
| 5792 | // Initialize cost to max so that VF = 2 is, at least, chosen during cost |
| 5793 | // evaluation. |
| 5794 | Cost = std::numeric_limits<float>::max(); |
| 5795 | } |
| 5796 | |
| 5797 | for (unsigned i = 2; i <= MaxVF.getFixedValue(); i *= 2) { |
| 5798 | // Notice that the vector loop needs to be executed less times, so |
| 5799 | // we need to divide the cost of the vector loops by the width of |
| 5800 | // the vector elements. |
| 5801 | VectorizationCostTy C = expectedCost(ElementCount::getFixed(i)); |
| 5802 | assert(C.first.isValid() && "Unexpected invalid cost for vector loop" ); |
| 5803 | float VectorCost = *C.first.getValue() / (float)i; |
| 5804 | LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << i |
| 5805 | << " costs: " << (int)VectorCost << ".\n" ); |
| 5806 | if (!C.second && !ForceVectorization) { |
| 5807 | LLVM_DEBUG( |
| 5808 | dbgs() << "LV: Not considering vector loop of width " << i |
| 5809 | << " because it will not generate any vector instructions.\n" ); |
| 5810 | continue; |
| 5811 | } |
| 5812 | |
| 5813 | // If profitable add it to ProfitableVF list. |
| 5814 | if (VectorCost < ScalarCost) { |
| 5815 | ProfitableVFs.push_back(VectorizationFactor( |
| 5816 | {ElementCount::getFixed(i), (unsigned)VectorCost})); |
| 5817 | } |
| 5818 | |
| 5819 | if (VectorCost < Cost) { |
| 5820 | Cost = VectorCost; |
| 5821 | Width = i; |
| 5822 | } |
| 5823 | } |
| 5824 | |
| 5825 | if (!EnableCondStoresVectorization && NumPredStores) { |
| 5826 | reportVectorizationFailure("There are conditional stores." , |
| 5827 | "store that is conditionally executed prevents vectorization" , |
| 5828 | "ConditionalStore" , ORE, TheLoop); |
| 5829 | Width = 1; |
| 5830 | Cost = ScalarCost; |
| 5831 | } |
| 5832 | |
| 5833 | LLVM_DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs() |
| 5834 | << "LV: Vectorization seems to be not beneficial, " |
| 5835 | << "but was forced by a user.\n" ); |
| 5836 | LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n" ); |
| 5837 | VectorizationFactor Factor = {ElementCount::getFixed(Width), |
| 5838 | (unsigned)(Width * Cost)}; |
| 5839 | return Factor; |
| 5840 | } |
| 5841 | |
| 5842 | bool LoopVectorizationCostModel::isCandidateForEpilogueVectorization( |
| 5843 | const Loop &L, ElementCount VF) const { |
| 5844 | // Cross iteration phis such as reductions need special handling and are |
| 5845 | // currently unsupported. |
| 5846 | if (any_of(L.getHeader()->phis(), [&](PHINode &Phi) { |
| 5847 | return Legal->isFirstOrderRecurrence(&Phi) || |
| 5848 | Legal->isReductionVariable(&Phi); |
| 5849 | })) |
| 5850 | return false; |
| 5851 | |
| 5852 | // Phis with uses outside of the loop require special handling and are |
| 5853 | // currently unsupported. |
| 5854 | for (auto &Entry : Legal->getInductionVars()) { |
| 5855 | // Look for uses of the value of the induction at the last iteration. |
| 5856 | Value *PostInc = Entry.first->getIncomingValueForBlock(L.getLoopLatch()); |
| 5857 | for (User *U : PostInc->users()) |
| 5858 | if (!L.contains(cast<Instruction>(U))) |
| 5859 | return false; |
| 5860 | // Look for uses of penultimate value of the induction. |
| 5861 | for (User *U : Entry.first->users()) |
| 5862 | if (!L.contains(cast<Instruction>(U))) |
| 5863 | return false; |
| 5864 | } |
| 5865 | |
| 5866 | // Induction variables that are widened require special handling that is |
| 5867 | // currently not supported. |
| 5868 | if (any_of(Legal->getInductionVars(), [&](auto &Entry) { |
| 5869 | return !(this->isScalarAfterVectorization(Entry.first, VF) || |
| 5870 | this->isProfitableToScalarize(Entry.first, VF)); |
| 5871 | })) |
| 5872 | return false; |
| 5873 | |
| 5874 | return true; |
| 5875 | } |
| 5876 | |
| 5877 | bool LoopVectorizationCostModel::isEpilogueVectorizationProfitable( |
| 5878 | const ElementCount VF) const { |
| 5879 | // FIXME: We need a much better cost-model to take different parameters such |
| 5880 | // as register pressure, code size increase and cost of extra branches into |
| 5881 | // account. For now we apply a very crude heuristic and only consider loops |
| 5882 | // with vectorization factors larger than a certain value. |
| 5883 | // We also consider epilogue vectorization unprofitable for targets that don't |
| 5884 | // consider interleaving beneficial (eg. MVE). |
| 5885 | if (TTI.getMaxInterleaveFactor(VF.getKnownMinValue()) <= 1) |
| 5886 | return false; |
| 5887 | if (VF.getFixedValue() >= EpilogueVectorizationMinVF) |
| 5888 | return true; |
| 5889 | return false; |
| 5890 | } |
| 5891 | |
| 5892 | VectorizationFactor |
| 5893 | LoopVectorizationCostModel::selectEpilogueVectorizationFactor( |
| 5894 | const ElementCount MainLoopVF, const LoopVectorizationPlanner &LVP) { |
| 5895 | VectorizationFactor Result = VectorizationFactor::Disabled(); |
| 5896 | if (!EnableEpilogueVectorization) { |
| 5897 | LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is disabled.\n" ;); |
| 5898 | return Result; |
| 5899 | } |
| 5900 | |
| 5901 | if (!isScalarEpilogueAllowed()) { |
| 5902 | LLVM_DEBUG( |
| 5903 | dbgs() << "LEV: Unable to vectorize epilogue because no epilogue is " |
| 5904 | "allowed.\n" ;); |
| 5905 | return Result; |
| 5906 | } |
| 5907 | |
| 5908 | // FIXME: This can be fixed for scalable vectors later, because at this stage |
| 5909 | // the LoopVectorizer will only consider vectorizing a loop with scalable |
| 5910 | // vectors when the loop has a hint to enable vectorization for a given VF. |
| 5911 | if (MainLoopVF.isScalable()) { |
| 5912 | LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization for scalable vectors not " |
| 5913 | "yet supported.\n" ); |
| 5914 | return Result; |
| 5915 | } |
| 5916 | |
| 5917 | // Not really a cost consideration, but check for unsupported cases here to |
| 5918 | // simplify the logic. |
| 5919 | if (!isCandidateForEpilogueVectorization(*TheLoop, MainLoopVF)) { |
| 5920 | LLVM_DEBUG( |
| 5921 | dbgs() << "LEV: Unable to vectorize epilogue because the loop is " |
| 5922 | "not a supported candidate.\n" ;); |
| 5923 | return Result; |
| 5924 | } |
| 5925 | |
| 5926 | if (EpilogueVectorizationForceVF > 1) { |
| 5927 | LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization factor is forced.\n" ;); |
| 5928 | if (LVP.hasPlanWithVFs( |
| 5929 | {MainLoopVF, ElementCount::getFixed(EpilogueVectorizationForceVF)})) |
| 5930 | return {ElementCount::getFixed(EpilogueVectorizationForceVF), 0}; |
| 5931 | else { |
| 5932 | LLVM_DEBUG( |
| 5933 | dbgs() |
| 5934 | << "LEV: Epilogue vectorization forced factor is not viable.\n" ;); |
| 5935 | return Result; |
| 5936 | } |
| 5937 | } |
| 5938 | |
| 5939 | if (TheLoop->getHeader()->getParent()->hasOptSize() || |
| 5940 | TheLoop->getHeader()->getParent()->hasMinSize()) { |
| 5941 | LLVM_DEBUG( |
| 5942 | dbgs() |
| 5943 | << "LEV: Epilogue vectorization skipped due to opt for size.\n" ;); |
| 5944 | return Result; |
| 5945 | } |
| 5946 | |
| 5947 | if (!isEpilogueVectorizationProfitable(MainLoopVF)) |
| 5948 | return Result; |
| 5949 | |
| 5950 | for (auto &NextVF : ProfitableVFs) |
| 5951 | if (ElementCount::isKnownLT(NextVF.Width, MainLoopVF) && |
| 5952 | (Result.Width.getFixedValue() == 1 || NextVF.Cost < Result.Cost) && |
| 5953 | LVP.hasPlanWithVFs({MainLoopVF, NextVF.Width})) |
| 5954 | Result = NextVF; |
| 5955 | |
| 5956 | if (Result != VectorizationFactor::Disabled()) |
| 5957 | LLVM_DEBUG(dbgs() << "LEV: Vectorizing epilogue loop with VF = " |
| 5958 | << Result.Width.getFixedValue() << "\n" ;); |
| 5959 | return Result; |
| 5960 | } |
| 5961 | |
| 5962 | std::pair<unsigned, unsigned> |
| 5963 | LoopVectorizationCostModel::getSmallestAndWidestTypes() { |
| 5964 | unsigned MinWidth = -1U; |
| 5965 | unsigned MaxWidth = 8; |
| 5966 | const DataLayout &DL = TheFunction->getParent()->getDataLayout(); |
| 5967 | |
| 5968 | // For each block. |
| 5969 | for (BasicBlock *BB : TheLoop->blocks()) { |
| 5970 | // For each instruction in the loop. |
| 5971 | for (Instruction &I : BB->instructionsWithoutDebug()) { |
| 5972 | Type *T = I.getType(); |
| 5973 | |
| 5974 | // Skip ignored values. |
| 5975 | if (ValuesToIgnore.count(&I)) |
| 5976 | continue; |
| 5977 | |
| 5978 | // Only examine Loads, Stores and PHINodes. |
| 5979 | if (!isa<LoadInst>(I) && !isa<StoreInst>(I) && !isa<PHINode>(I)) |
| 5980 | continue; |
| 5981 | |
| 5982 | // Examine PHI nodes that are reduction variables. Update the type to |
| 5983 | // account for the recurrence type. |
| 5984 | if (auto *PN = dyn_cast<PHINode>(&I)) { |
| 5985 | if (!Legal->isReductionVariable(PN)) |
| 5986 | continue; |
| 5987 | RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[PN]; |
| 5988 | if (PreferInLoopReductions || |
| 5989 | TTI.preferInLoopReduction(RdxDesc.getOpcode(), |
| 5990 | RdxDesc.getRecurrenceType(), |
| 5991 | TargetTransformInfo::ReductionFlags())) |
| 5992 | continue; |
| 5993 | T = RdxDesc.getRecurrenceType(); |
| 5994 | } |
| 5995 | |
| 5996 | // Examine the stored values. |
| 5997 | if (auto *ST = dyn_cast<StoreInst>(&I)) |
| 5998 | T = ST->getValueOperand()->getType(); |
| 5999 | |
| 6000 | // Ignore loaded pointer types and stored pointer types that are not |
| 6001 | // vectorizable. |
| 6002 | // |
| 6003 | // FIXME: The check here attempts to predict whether a load or store will |
| 6004 | // be vectorized. We only know this for certain after a VF has |
| 6005 | // been selected. Here, we assume that if an access can be |
| 6006 | // vectorized, it will be. We should also look at extending this |
| 6007 | // optimization to non-pointer types. |
| 6008 | // |
| 6009 | if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) && |
| 6010 | !isAccessInterleaved(&I) && !isLegalGatherOrScatter(&I)) |
| 6011 | continue; |
| 6012 | |
| 6013 | MinWidth = std::min(MinWidth, |
| 6014 | (unsigned)DL.getTypeSizeInBits(T->getScalarType())); |
| 6015 | MaxWidth = std::max(MaxWidth, |
| 6016 | (unsigned)DL.getTypeSizeInBits(T->getScalarType())); |
| 6017 | } |
| 6018 | } |
| 6019 | |
| 6020 | return {MinWidth, MaxWidth}; |
| 6021 | } |
| 6022 | |
| 6023 | unsigned LoopVectorizationCostModel::selectInterleaveCount(ElementCount VF, |
| 6024 | unsigned LoopCost) { |
| 6025 | // -- The interleave heuristics -- |
| 6026 | // We interleave the loop in order to expose ILP and reduce the loop overhead. |
| 6027 | // There are many micro-architectural considerations that we can't predict |
| 6028 | // at this level. For example, frontend pressure (on decode or fetch) due to |
| 6029 | // code size, or the number and capabilities of the execution ports. |
| 6030 | // |
| 6031 | // We use the following heuristics to select the interleave count: |
| 6032 | // 1. If the code has reductions, then we interleave to break the cross |
| 6033 | // iteration dependency. |
| 6034 | // 2. If the loop is really small, then we interleave to reduce the loop |
| 6035 | // overhead. |
| 6036 | // 3. We don't interleave if we think that we will spill registers to memory |
| 6037 | // due to the increased register pressure. |
| 6038 | |
| 6039 | if (!isScalarEpilogueAllowed()) |
| 6040 | return 1; |
| 6041 | |
| 6042 | // We used the distance for the interleave count. |
| 6043 | if (Legal->getMaxSafeDepDistBytes() != -1U) |
| 6044 | return 1; |
| 6045 | |
| 6046 | auto BestKnownTC = getSmallBestKnownTC(*PSE.getSE(), TheLoop); |
| 6047 | const bool HasReductions = !Legal->getReductionVars().empty(); |
| 6048 | // Do not interleave loops with a relatively small known or estimated trip |
| 6049 | // count. But we will interleave when InterleaveSmallLoopScalarReduction is |
| 6050 | // enabled, and the code has scalar reductions(HasReductions && VF = 1), |
| 6051 | // because with the above conditions interleaving can expose ILP and break |
| 6052 | // cross iteration dependences for reductions. |
| 6053 | if (BestKnownTC && (*BestKnownTC < TinyTripCountInterleaveThreshold) && |
| 6054 | !(InterleaveSmallLoopScalarReduction && HasReductions && VF.isScalar())) |
| 6055 | return 1; |
| 6056 | |
| 6057 | RegisterUsage R = calculateRegisterUsage({VF})[0]; |
| 6058 | // We divide by these constants so assume that we have at least one |
| 6059 | // instruction that uses at least one register. |
| 6060 | for (auto& pair : R.MaxLocalUsers) { |
| 6061 | pair.second = std::max(pair.second, 1U); |
| 6062 | } |
| 6063 | |
| 6064 | // We calculate the interleave count using the following formula. |
| 6065 | // Subtract the number of loop invariants from the number of available |
| 6066 | // registers. These registers are used by all of the interleaved instances. |
| 6067 | // Next, divide the remaining registers by the number of registers that is |
| 6068 | // required by the loop, in order to estimate how many parallel instances |
| 6069 | // fit without causing spills. All of this is rounded down if necessary to be |
| 6070 | // a power of two. We want power of two interleave count to simplify any |
| 6071 | // addressing operations or alignment considerations. |
| 6072 | // We also want power of two interleave counts to ensure that the induction |
| 6073 | // variable of the vector loop wraps to zero, when tail is folded by masking; |
| 6074 | // this currently happens when OptForSize, in which case IC is set to 1 above. |
| 6075 | unsigned IC = UINT_MAX; |
| 6076 | |
| 6077 | for (auto& pair : R.MaxLocalUsers) { |
| 6078 | unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); |
| 6079 | LLVM_DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters |
| 6080 | << " registers of " |
| 6081 | << TTI.getRegisterClassName(pair.first) << " register class\n" ); |
| 6082 | if (VF.isScalar()) { |
| 6083 | if (ForceTargetNumScalarRegs.getNumOccurrences() > 0) |
| 6084 | TargetNumRegisters = ForceTargetNumScalarRegs; |
| 6085 | } else { |
| 6086 | if (ForceTargetNumVectorRegs.getNumOccurrences() > 0) |
| 6087 | TargetNumRegisters = ForceTargetNumVectorRegs; |
| 6088 | } |
| 6089 | unsigned MaxLocalUsers = pair.second; |
| 6090 | unsigned LoopInvariantRegs = 0; |
| 6091 | if (R.LoopInvariantRegs.find(pair.first) != R.LoopInvariantRegs.end()) |
| 6092 | LoopInvariantRegs = R.LoopInvariantRegs[pair.first]; |
| 6093 | |
| 6094 | unsigned TmpIC = PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs) / MaxLocalUsers); |
| 6095 | // Don't count the induction variable as interleaved. |
| 6096 | if (EnableIndVarRegisterHeur) { |
| 6097 | TmpIC = |
| 6098 | PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs - 1) / |
| 6099 | std::max(1U, (MaxLocalUsers - 1))); |
| 6100 | } |
| 6101 | |
| 6102 | IC = std::min(IC, TmpIC); |
| 6103 | } |
| 6104 | |
| 6105 | // Clamp the interleave ranges to reasonable counts. |
| 6106 | unsigned MaxInterleaveCount = |
| 6107 | TTI.getMaxInterleaveFactor(VF.getKnownMinValue()); |
| 6108 | |
| 6109 | // Check if the user has overridden the max. |
| 6110 | if (VF.isScalar()) { |
| 6111 | if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0) |
| 6112 | MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor; |
| 6113 | } else { |
| 6114 | if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0) |
| 6115 | MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor; |
| 6116 | } |
| 6117 | |
| 6118 | // If trip count is known or estimated compile time constant, limit the |
| 6119 | // interleave count to be less than the trip count divided by VF, provided it |
| 6120 | // is at least 1. |
| 6121 | // |
| 6122 | // For scalable vectors we can't know if interleaving is beneficial. It may |
| 6123 | // not be beneficial for small loops if none of the lanes in the second vector |
| 6124 | // iterations is enabled. However, for larger loops, there is likely to be a |
| 6125 | // similar benefit as for fixed-width vectors. For now, we choose to leave |
| 6126 | // the InterleaveCount as if vscale is '1', although if some information about |
| 6127 | // the vector is known (e.g. min vector size), we can make a better decision. |
| 6128 | if (BestKnownTC) { |
| 6129 | MaxInterleaveCount = |
| 6130 | std::min(*BestKnownTC / VF.getKnownMinValue(), MaxInterleaveCount); |
| 6131 | // Make sure MaxInterleaveCount is greater than 0. |
| 6132 | MaxInterleaveCount = std::max(1u, MaxInterleaveCount); |
| 6133 | } |
| 6134 | |
| 6135 | assert(MaxInterleaveCount > 0 && |
| 6136 | "Maximum interleave count must be greater than 0" ); |
| 6137 | |
| 6138 | // Clamp the calculated IC to be between the 1 and the max interleave count |
| 6139 | // that the target and trip count allows. |
| 6140 | if (IC > MaxInterleaveCount) |
| 6141 | IC = MaxInterleaveCount; |
| 6142 | else |
| 6143 | // Make sure IC is greater than 0. |
| 6144 | IC = std::max(1u, IC); |
| 6145 | |
| 6146 | assert(IC > 0 && "Interleave count must be greater than 0." ); |
| 6147 | |
| 6148 | // If we did not calculate the cost for VF (because the user selected the VF) |
| 6149 | // then we calculate the cost of VF here. |
| 6150 | if (LoopCost == 0) { |
| 6151 | assert(expectedCost(VF).first.isValid() && "Expected a valid cost" ); |
| 6152 | LoopCost = *expectedCost(VF).first.getValue(); |
| 6153 | } |
| 6154 | |
| 6155 | assert(LoopCost && "Non-zero loop cost expected" ); |
| 6156 | |
| 6157 | // Interleave if we vectorized this loop and there is a reduction that could |
| 6158 | // benefit from interleaving. |
| 6159 | if (VF.isVector() && HasReductions) { |
| 6160 | LLVM_DEBUG(dbgs() << "LV: Interleaving because of reductions.\n" ); |
| 6161 | return IC; |
| 6162 | } |
| 6163 | |
| 6164 | // Note that if we've already vectorized the loop we will have done the |
| 6165 | // runtime check and so interleaving won't require further checks. |
| 6166 | bool InterleavingRequiresRuntimePointerCheck = |
| 6167 | (VF.isScalar() && Legal->getRuntimePointerChecking()->Need); |
| 6168 | |
| 6169 | // We want to interleave small loops in order to reduce the loop overhead and |
| 6170 | // potentially expose ILP opportunities. |
| 6171 | LLVM_DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n' |
| 6172 | << "LV: IC is " << IC << '\n' |
| 6173 | << "LV: VF is " << VF << '\n'); |
| 6174 | const bool AggressivelyInterleaveReductions = |
| 6175 | TTI.enableAggressiveInterleaving(HasReductions); |
| 6176 | if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) { |
| 6177 | // We assume that the cost overhead is 1 and we use the cost model |
| 6178 | // to estimate the cost of the loop and interleave until the cost of the |
| 6179 | // loop overhead is about 5% of the cost of the loop. |
| 6180 | unsigned SmallIC = |
| 6181 | std::min(IC, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost)); |
| 6182 | |
| 6183 | // Interleave until store/load ports (estimated by max interleave count) are |
| 6184 | // saturated. |
| 6185 | unsigned NumStores = Legal->getNumStores(); |
| 6186 | unsigned NumLoads = Legal->getNumLoads(); |
| 6187 | unsigned StoresIC = IC / (NumStores ? NumStores : 1); |
| 6188 | unsigned LoadsIC = IC / (NumLoads ? NumLoads : 1); |
| 6189 | |
| 6190 | // If we have a scalar reduction (vector reductions are already dealt with |
| 6191 | // by this point), we can increase the critical path length if the loop |
| 6192 | // we're interleaving is inside another loop. Limit, by default to 2, so the |
| 6193 | // critical path only gets increased by one reduction operation. |
| 6194 | if (HasReductions && TheLoop->getLoopDepth() > 1) { |
| 6195 | unsigned F = static_cast<unsigned>(MaxNestedScalarReductionIC); |
| 6196 | SmallIC = std::min(SmallIC, F); |
| 6197 | StoresIC = std::min(StoresIC, F); |
| 6198 | LoadsIC = std::min(LoadsIC, F); |
| 6199 | } |
| 6200 | |
| 6201 | if (EnableLoadStoreRuntimeInterleave && |
| 6202 | std::max(StoresIC, LoadsIC) > SmallIC) { |
| 6203 | LLVM_DEBUG( |
| 6204 | dbgs() << "LV: Interleaving to saturate store or load ports.\n" ); |
| 6205 | return std::max(StoresIC, LoadsIC); |
| 6206 | } |
| 6207 | |
| 6208 | // If there are scalar reductions and TTI has enabled aggressive |
| 6209 | // interleaving for reductions, we will interleave to expose ILP. |
| 6210 | if (InterleaveSmallLoopScalarReduction && VF.isScalar() && |
| 6211 | AggressivelyInterleaveReductions) { |
| 6212 | LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n" ); |
| 6213 | // Interleave no less than SmallIC but not as aggressive as the normal IC |
| 6214 | // to satisfy the rare situation when resources are too limited. |
| 6215 | return std::max(IC / 2, SmallIC); |
| 6216 | } else { |
| 6217 | LLVM_DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n" ); |
| 6218 | return SmallIC; |
| 6219 | } |
| 6220 | } |
| 6221 | |
| 6222 | // Interleave if this is a large loop (small loops are already dealt with by |
| 6223 | // this point) that could benefit from interleaving. |
| 6224 | if (AggressivelyInterleaveReductions) { |
| 6225 | LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n" ); |
| 6226 | return IC; |
| 6227 | } |
| 6228 | |
| 6229 | LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n" ); |
| 6230 | return 1; |
| 6231 | } |
| 6232 | |
| 6233 | SmallVector<LoopVectorizationCostModel::RegisterUsage, 8> |
| 6234 | LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) { |
| 6235 | // This function calculates the register usage by measuring the highest number |
| 6236 | // of values that are alive at a single location. Obviously, this is a very |
| 6237 | // rough estimation. We scan the loop in a topological order in order and |
| 6238 | // assign a number to each instruction. We use RPO to ensure that defs are |
| 6239 | // met before their users. We assume that each instruction that has in-loop |
| 6240 | // users starts an interval. We record every time that an in-loop value is |
| 6241 | // used, so we have a list of the first and last occurrences of each |
| 6242 | // instruction. Next, we transpose this data structure into a multi map that |
| 6243 | // holds the list of intervals that *end* at a specific location. This multi |
| 6244 | // map allows us to perform a linear search. We scan the instructions linearly |
| 6245 | // and record each time that a new interval starts, by placing it in a set. |
| 6246 | // If we find this value in the multi-map then we remove it from the set. |
| 6247 | // The max register usage is the maximum size of the set. |
| 6248 | // We also search for instructions that are defined outside the loop, but are |
| 6249 | // used inside the loop. We need this number separately from the max-interval |
| 6250 | // usage number because when we unroll, loop-invariant values do not take |
| 6251 | // more register. |
| 6252 | LoopBlocksDFS DFS(TheLoop); |
| 6253 | DFS.perform(LI); |
| 6254 | |
| 6255 | RegisterUsage RU; |
| 6256 | |
| 6257 | // Each 'key' in the map opens a new interval. The values |
| 6258 | // of the map are the index of the 'last seen' usage of the |
| 6259 | // instruction that is the key. |
| 6260 | using IntervalMap = DenseMap<Instruction *, unsigned>; |
| 6261 | |
| 6262 | // Maps instruction to its index. |
| 6263 | SmallVector<Instruction *, 64> IdxToInstr; |
| 6264 | // Marks the end of each interval. |
| 6265 | IntervalMap EndPoint; |
| 6266 | // Saves the list of instruction indices that are used in the loop. |
| 6267 | SmallPtrSet<Instruction *, 8> Ends; |
| 6268 | // Saves the list of values that are used in the loop but are |
| 6269 | // defined outside the loop, such as arguments and constants. |
| 6270 | SmallPtrSet<Value *, 8> LoopInvariants; |
| 6271 | |
| 6272 | for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) { |
| 6273 | for (Instruction &I : BB->instructionsWithoutDebug()) { |
| 6274 | IdxToInstr.push_back(&I); |
| 6275 | |
| 6276 | // Save the end location of each USE. |
| 6277 | for (Value *U : I.operands()) { |
| 6278 | auto *Instr = dyn_cast<Instruction>(U); |
| 6279 | |
| 6280 | // Ignore non-instruction values such as arguments, constants, etc. |
| 6281 | if (!Instr) |
| 6282 | continue; |
| 6283 | |
| 6284 | // If this instruction is outside the loop then record it and continue. |
| 6285 | if (!TheLoop->contains(Instr)) { |
| 6286 | LoopInvariants.insert(Instr); |
| 6287 | continue; |
| 6288 | } |
| 6289 | |
| 6290 | // Overwrite previous end points. |
| 6291 | EndPoint[Instr] = IdxToInstr.size(); |
| 6292 | Ends.insert(Instr); |
| 6293 | } |
| 6294 | } |
| 6295 | } |
| 6296 | |
| 6297 | // Saves the list of intervals that end with the index in 'key'. |
| 6298 | using InstrList = SmallVector<Instruction *, 2>; |
| 6299 | DenseMap<unsigned, InstrList> TransposeEnds; |
| 6300 | |
| 6301 | // Transpose the EndPoints to a list of values that end at each index. |
| 6302 | for (auto &Interval : EndPoint) |
| 6303 | TransposeEnds[Interval.second].push_back(Interval.first); |
| 6304 | |
| 6305 | SmallPtrSet<Instruction *, 8> OpenIntervals; |
| 6306 | SmallVector<RegisterUsage, 8> RUs(VFs.size()); |
| 6307 | SmallVector<SmallMapVector<unsigned, unsigned, 4>, 8> MaxUsages(VFs.size()); |
| 6308 | |
| 6309 | LLVM_DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n" ); |
| 6310 | |
| 6311 | // A lambda that gets the register usage for the given type and VF. |
| 6312 | const auto &TTICapture = TTI; |
| 6313 | auto GetRegUsage = [&TTICapture](Type *Ty, ElementCount VF) { |
| 6314 | if (Ty->isTokenTy() || !VectorType::isValidElementType(Ty)) |
| 6315 | return 0U; |
| 6316 | return TTICapture.getRegUsageForType(VectorType::get(Ty, VF)); |
| 6317 | }; |
| 6318 | |
| 6319 | for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) { |
| 6320 | Instruction *I = IdxToInstr[i]; |
| 6321 | |
| 6322 | // Remove all of the instructions that end at this location. |
| 6323 | InstrList &List = TransposeEnds[i]; |
| 6324 | for (Instruction *ToRemove : List) |
| 6325 | OpenIntervals.erase(ToRemove); |
| 6326 | |
| 6327 | // Ignore instructions that are never used within the loop. |
| 6328 | if (!Ends.count(I)) |
| 6329 | continue; |
| 6330 | |
| 6331 | // Skip ignored values. |
| 6332 | if (ValuesToIgnore.count(I)) |
| 6333 | continue; |
| 6334 | |
| 6335 | // For each VF find the maximum usage of registers. |
| 6336 | for (unsigned j = 0, e = VFs.size(); j < e; ++j) { |
| 6337 | // Count the number of live intervals. |
| 6338 | SmallMapVector<unsigned, unsigned, 4> RegUsage; |
| 6339 | |
| 6340 | if (VFs[j].isScalar()) { |
| 6341 | for (auto Inst : OpenIntervals) { |
| 6342 | unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType()); |
| 6343 | if (RegUsage.find(ClassID) == RegUsage.end()) |
| 6344 | RegUsage[ClassID] = 1; |
| 6345 | else |
| 6346 | RegUsage[ClassID] += 1; |
| 6347 | } |
| 6348 | } else { |
| 6349 | collectUniformsAndScalars(VFs[j]); |
| 6350 | for (auto Inst : OpenIntervals) { |
| 6351 | // Skip ignored values for VF > 1. |
| 6352 | if (VecValuesToIgnore.count(Inst)) |
| 6353 | continue; |
| 6354 | if (isScalarAfterVectorization(Inst, VFs[j])) { |
| 6355 | unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType()); |
| 6356 | if (RegUsage.find(ClassID) == RegUsage.end()) |
| 6357 | RegUsage[ClassID] = 1; |
| 6358 | else |
| 6359 | RegUsage[ClassID] += 1; |
| 6360 | } else { |
| 6361 | unsigned ClassID = TTI.getRegisterClassForType(true, Inst->getType()); |
| 6362 | if (RegUsage.find(ClassID) == RegUsage.end()) |
| 6363 | RegUsage[ClassID] = GetRegUsage(Inst->getType(), VFs[j]); |
| 6364 | else |
| 6365 | RegUsage[ClassID] += GetRegUsage(Inst->getType(), VFs[j]); |
| 6366 | } |
| 6367 | } |
| 6368 | } |
| 6369 | |
| 6370 | for (auto& pair : RegUsage) { |
| 6371 | if (MaxUsages[j].find(pair.first) != MaxUsages[j].end()) |
| 6372 | MaxUsages[j][pair.first] = std::max(MaxUsages[j][pair.first], pair.second); |
| 6373 | else |
| 6374 | MaxUsages[j][pair.first] = pair.second; |
| 6375 | } |
| 6376 | } |
| 6377 | |
| 6378 | LLVM_DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " |
| 6379 | << OpenIntervals.size() << '\n'); |
| 6380 | |
| 6381 | // Add the current instruction to the list of open intervals. |
| 6382 | OpenIntervals.insert(I); |
| 6383 | } |
| 6384 | |
| 6385 | for (unsigned i = 0, e = VFs.size(); i < e; ++i) { |
| 6386 | SmallMapVector<unsigned, unsigned, 4> Invariant; |
| 6387 | |
| 6388 | for (auto Inst : LoopInvariants) { |
| 6389 | unsigned Usage = |
| 6390 | VFs[i].isScalar() ? 1 : GetRegUsage(Inst->getType(), VFs[i]); |
| 6391 | unsigned ClassID = |
| 6392 | TTI.getRegisterClassForType(VFs[i].isVector(), Inst->getType()); |
| 6393 | if (Invariant.find(ClassID) == Invariant.end()) |
| 6394 | Invariant[ClassID] = Usage; |
| 6395 | else |
| 6396 | Invariant[ClassID] += Usage; |
| 6397 | } |
| 6398 | |
| 6399 | LLVM_DEBUG({ |
| 6400 | dbgs() << "LV(REG): VF = " << VFs[i] << '\n'; |
| 6401 | dbgs() << "LV(REG): Found max usage: " << MaxUsages[i].size() |
| 6402 | << " item\n" ; |
| 6403 | for (const auto &pair : MaxUsages[i]) { |
| 6404 | dbgs() << "LV(REG): RegisterClass: " |
| 6405 | << TTI.getRegisterClassName(pair.first) << ", " << pair.second |
| 6406 | << " registers\n" ; |
| 6407 | } |
| 6408 | dbgs() << "LV(REG): Found invariant usage: " << Invariant.size() |
| 6409 | << " item\n" ; |
| 6410 | for (const auto &pair : Invariant) { |
| 6411 | dbgs() << "LV(REG): RegisterClass: " |
| 6412 | << TTI.getRegisterClassName(pair.first) << ", " << pair.second |
| 6413 | << " registers\n" ; |
| 6414 | } |
| 6415 | }); |
| 6416 | |
| 6417 | RU.LoopInvariantRegs = Invariant; |
| 6418 | RU.MaxLocalUsers = MaxUsages[i]; |
| 6419 | RUs[i] = RU; |
| 6420 | } |
| 6421 | |
| 6422 | return RUs; |
| 6423 | } |
| 6424 | |
| 6425 | bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I){ |
| 6426 | // TODO: Cost model for emulated masked load/store is completely |
| 6427 | // broken. This hack guides the cost model to use an artificially |
| 6428 | // high enough value to practically disable vectorization with such |
| 6429 | // operations, except where previously deployed legality hack allowed |
| 6430 | // using very low cost values. This is to avoid regressions coming simply |
| 6431 | // from moving "masked load/store" check from legality to cost model. |
| 6432 | // Masked Load/Gather emulation was previously never allowed. |
| 6433 | // Limited number of Masked Store/Scatter emulation was allowed. |
| 6434 | assert(isPredicatedInst(I) && "Expecting a scalar emulated instruction" ); |
| 6435 | return isa<LoadInst>(I) || |
| 6436 | (isa<StoreInst>(I) && |
| 6437 | NumPredStores > NumberOfStoresToPredicate); |
| 6438 | } |
| 6439 | |
| 6440 | void LoopVectorizationCostModel::collectInstsToScalarize(ElementCount VF) { |
| 6441 | // If we aren't vectorizing the loop, or if we've already collected the |
| 6442 | // instructions to scalarize, there's nothing to do. Collection may already |
| 6443 | // have occurred if we have a user-selected VF and are now computing the |
| 6444 | // expected cost for interleaving. |
| 6445 | if (VF.isScalar() || VF.isZero() || |
| 6446 | InstsToScalarize.find(VF) != InstsToScalarize.end()) |
| 6447 | return; |
| 6448 | |
| 6449 | // Initialize a mapping for VF in InstsToScalalarize. If we find that it's |
| 6450 | // not profitable to scalarize any instructions, the presence of VF in the |
| 6451 | // map will indicate that we've analyzed it already. |
| 6452 | ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF]; |
| 6453 | |
| 6454 | // Find all the instructions that are scalar with predication in the loop and |
| 6455 | // determine if it would be better to not if-convert the blocks they are in. |
| 6456 | // If so, we also record the instructions to scalarize. |
| 6457 | for (BasicBlock *BB : TheLoop->blocks()) { |
| 6458 | if (!blockNeedsPredication(BB)) |
| 6459 | continue; |
| 6460 | for (Instruction &I : *BB) |
| 6461 | if (isScalarWithPredication(&I)) { |
| 6462 | ScalarCostsTy ScalarCosts; |
| 6463 | // Do not apply discount logic if hacked cost is needed |
| 6464 | // for emulated masked memrefs. |
| 6465 | if (!useEmulatedMaskMemRefHack(&I) && |
| 6466 | computePredInstDiscount(&I, ScalarCosts, VF) >= 0) |
| 6467 | ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end()); |
| 6468 | // Remember that BB will remain after vectorization. |
| 6469 | PredicatedBBsAfterVectorization.insert(BB); |
| 6470 | } |
| 6471 | } |
| 6472 | } |
| 6473 | |
| 6474 | int LoopVectorizationCostModel::computePredInstDiscount( |
| 6475 | Instruction *PredInst, ScalarCostsTy &ScalarCosts, ElementCount VF) { |
| 6476 | assert(!isUniformAfterVectorization(PredInst, VF) && |
| 6477 | "Instruction marked uniform-after-vectorization will be predicated" ); |
| 6478 | |
| 6479 | // Initialize the discount to zero, meaning that the scalar version and the |
| 6480 | // vector version cost the same. |
| 6481 | InstructionCost Discount = 0; |
| 6482 | |
| 6483 | // Holds instructions to analyze. The instructions we visit are mapped in |
| 6484 | // ScalarCosts. Those instructions are the ones that would be scalarized if |
| 6485 | // we find that the scalar version costs less. |
| 6486 | SmallVector<Instruction *, 8> Worklist; |
| 6487 | |
| 6488 | // Returns true if the given instruction can be scalarized. |
| 6489 | auto canBeScalarized = [&](Instruction *I) -> bool { |
| 6490 | // We only attempt to scalarize instructions forming a single-use chain |
| 6491 | // from the original predicated block that would otherwise be vectorized. |
| 6492 | // Although not strictly necessary, we give up on instructions we know will |
| 6493 | // already be scalar to avoid traversing chains that are unlikely to be |
| 6494 | // beneficial. |
| 6495 | if (!I->hasOneUse() || PredInst->getParent() != I->getParent() || |
| 6496 | isScalarAfterVectorization(I, VF)) |
| 6497 | return false; |
| 6498 | |
| 6499 | // If the instruction is scalar with predication, it will be analyzed |
| 6500 | // separately. We ignore it within the context of PredInst. |
| 6501 | if (isScalarWithPredication(I)) |
| 6502 | return false; |
| 6503 | |
| 6504 | // If any of the instruction's operands are uniform after vectorization, |
| 6505 | // the instruction cannot be scalarized. This prevents, for example, a |
| 6506 | // masked load from being scalarized. |
| 6507 | // |
| 6508 | // We assume we will only emit a value for lane zero of an instruction |
| 6509 | // marked uniform after vectorization, rather than VF identical values. |
| 6510 | // Thus, if we scalarize an instruction that uses a uniform, we would |
| 6511 | // create uses of values corresponding to the lanes we aren't emitting code |
| 6512 | // for. This behavior can be changed by allowing getScalarValue to clone |
| 6513 | // the lane zero values for uniforms rather than asserting. |
| 6514 | for (Use &U : I->operands()) |
| 6515 | if (auto *J = dyn_cast<Instruction>(U.get())) |
| 6516 | if (isUniformAfterVectorization(J, VF)) |
| 6517 | return false; |
| 6518 | |
| 6519 | // Otherwise, we can scalarize the instruction. |
| 6520 | return true; |
| 6521 | }; |
| 6522 | |
| 6523 | // Compute the expected cost discount from scalarizing the entire expression |
| 6524 | // feeding the predicated instruction. We currently only consider expressions |
| 6525 | // that are single-use instruction chains. |
| 6526 | Worklist.push_back(PredInst); |
| 6527 | while (!Worklist.empty()) { |
| 6528 | Instruction *I = Worklist.pop_back_val(); |
| 6529 | |
| 6530 | // If we've already analyzed the instruction, there's nothing to do. |
| 6531 | if (ScalarCosts.find(I) != ScalarCosts.end()) |
| 6532 | continue; |
| 6533 | |
| 6534 | // Compute the cost of the vector instruction. Note that this cost already |
| 6535 | // includes the scalarization overhead of the predicated instruction. |
| 6536 | InstructionCost VectorCost = getInstructionCost(I, VF).first; |
| 6537 | |
| 6538 | // Compute the cost of the scalarized instruction. This cost is the cost of |
| 6539 | // the instruction as if it wasn't if-converted and instead remained in the |
| 6540 | // predicated block. We will scale this cost by block probability after |
| 6541 | // computing the scalarization overhead. |
| 6542 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 6543 | InstructionCost ScalarCost = |
| 6544 | VF.getKnownMinValue() * |
| 6545 | getInstructionCost(I, ElementCount::getFixed(1)).first; |
| 6546 | |
| 6547 | // Compute the scalarization overhead of needed insertelement instructions |
| 6548 | // and phi nodes. |
| 6549 | if (isScalarWithPredication(I) && !I->getType()->isVoidTy()) { |
| 6550 | ScalarCost += TTI.getScalarizationOverhead( |
| 6551 | cast<VectorType>(ToVectorTy(I->getType(), VF)), |
| 6552 | APInt::getAllOnesValue(VF.getKnownMinValue()), true, false); |
| 6553 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 6554 | ScalarCost += |
| 6555 | VF.getKnownMinValue() * |
| 6556 | TTI.getCFInstrCost(Instruction::PHI, TTI::TCK_RecipThroughput); |
| 6557 | } |
| 6558 | |
| 6559 | // Compute the scalarization overhead of needed extractelement |
| 6560 | // instructions. For each of the instruction's operands, if the operand can |
| 6561 | // be scalarized, add it to the worklist; otherwise, account for the |
| 6562 | // overhead. |
| 6563 | for (Use &U : I->operands()) |
| 6564 | if (auto *J = dyn_cast<Instruction>(U.get())) { |
| 6565 | assert(VectorType::isValidElementType(J->getType()) && |
| 6566 | "Instruction has non-scalar type" ); |
| 6567 | if (canBeScalarized(J)) |
| 6568 | Worklist.push_back(J); |
| 6569 | else if (needsExtract(J, VF)) { |
| 6570 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 6571 | ScalarCost += TTI.getScalarizationOverhead( |
| 6572 | cast<VectorType>(ToVectorTy(J->getType(), VF)), |
| 6573 | APInt::getAllOnesValue(VF.getKnownMinValue()), false, true); |
| 6574 | } |
| 6575 | } |
| 6576 | |
| 6577 | // Scale the total scalar cost by block probability. |
| 6578 | ScalarCost /= getReciprocalPredBlockProb(); |
| 6579 | |
| 6580 | // Compute the discount. A non-negative discount means the vector version |
| 6581 | // of the instruction costs more, and scalarizing would be beneficial. |
| 6582 | Discount += VectorCost - ScalarCost; |
| 6583 | ScalarCosts[I] = ScalarCost; |
| 6584 | } |
| 6585 | |
| 6586 | return *Discount.getValue(); |
| 6587 | } |
| 6588 | |
| 6589 | LoopVectorizationCostModel::VectorizationCostTy |
| 6590 | LoopVectorizationCostModel::expectedCost(ElementCount VF) { |
| 6591 | VectorizationCostTy Cost; |
| 6592 | |
| 6593 | // For each block. |
| 6594 | for (BasicBlock *BB : TheLoop->blocks()) { |
| 6595 | VectorizationCostTy BlockCost; |
| 6596 | |
| 6597 | // For each instruction in the old loop. |
| 6598 | for (Instruction &I : BB->instructionsWithoutDebug()) { |
| 6599 | // Skip ignored values. |
| 6600 | if (ValuesToIgnore.count(&I) || |
| 6601 | (VF.isVector() && VecValuesToIgnore.count(&I))) |
| 6602 | continue; |
| 6603 | |
| 6604 | VectorizationCostTy C = getInstructionCost(&I, VF); |
| 6605 | |
| 6606 | // Check if we should override the cost. |
| 6607 | if (ForceTargetInstructionCost.getNumOccurrences() > 0) |
| 6608 | C.first = InstructionCost(ForceTargetInstructionCost); |
| 6609 | |
| 6610 | BlockCost.first += C.first; |
| 6611 | BlockCost.second |= C.second; |
| 6612 | LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first |
| 6613 | << " for VF " << VF << " For instruction: " << I |
| 6614 | << '\n'); |
| 6615 | } |
| 6616 | |
| 6617 | // If we are vectorizing a predicated block, it will have been |
| 6618 | // if-converted. This means that the block's instructions (aside from |
| 6619 | // stores and instructions that may divide by zero) will now be |
| 6620 | // unconditionally executed. For the scalar case, we may not always execute |
| 6621 | // the predicated block, if it is an if-else block. Thus, scale the block's |
| 6622 | // cost by the probability of executing it. blockNeedsPredication from |
| 6623 | // Legal is used so as to not include all blocks in tail folded loops. |
| 6624 | if (VF.isScalar() && Legal->blockNeedsPredication(BB)) |
| 6625 | BlockCost.first /= getReciprocalPredBlockProb(); |
| 6626 | |
| 6627 | Cost.first += BlockCost.first; |
| 6628 | Cost.second |= BlockCost.second; |
| 6629 | } |
| 6630 | |
| 6631 | return Cost; |
| 6632 | } |
| 6633 | |
| 6634 | /// Gets Address Access SCEV after verifying that the access pattern |
| 6635 | /// is loop invariant except the induction variable dependence. |
| 6636 | /// |
| 6637 | /// This SCEV can be sent to the Target in order to estimate the address |
| 6638 | /// calculation cost. |
| 6639 | static const SCEV *getAddressAccessSCEV( |
| 6640 | Value *Ptr, |
| 6641 | LoopVectorizationLegality *Legal, |
| 6642 | PredicatedScalarEvolution &PSE, |
| 6643 | const Loop *TheLoop) { |
| 6644 | |
| 6645 | auto *Gep = dyn_cast<GetElementPtrInst>(Ptr); |
| 6646 | if (!Gep) |
| 6647 | return nullptr; |
| 6648 | |
| 6649 | // We are looking for a gep with all loop invariant indices except for one |
| 6650 | // which should be an induction variable. |
| 6651 | auto SE = PSE.getSE(); |
| 6652 | unsigned NumOperands = Gep->getNumOperands(); |
| 6653 | for (unsigned i = 1; i < NumOperands; ++i) { |
| 6654 | Value *Opd = Gep->getOperand(i); |
| 6655 | if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) && |
| 6656 | !Legal->isInductionVariable(Opd)) |
| 6657 | return nullptr; |
| 6658 | } |
| 6659 | |
| 6660 | // Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV. |
| 6661 | return PSE.getSCEV(Ptr); |
| 6662 | } |
| 6663 | |
| 6664 | static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) { |
| 6665 | return Legal->hasStride(I->getOperand(0)) || |
| 6666 | Legal->hasStride(I->getOperand(1)); |
| 6667 | } |
| 6668 | |
| 6669 | InstructionCost |
| 6670 | LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I, |
| 6671 | ElementCount VF) { |
| 6672 | assert(VF.isVector() && |
| 6673 | "Scalarization cost of instruction implies vectorization." ); |
| 6674 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 6675 | Type *ValTy = getMemInstValueType(I); |
| 6676 | auto SE = PSE.getSE(); |
| 6677 | |
| 6678 | unsigned AS = getLoadStoreAddressSpace(I); |
| 6679 | Value *Ptr = getLoadStorePointerOperand(I); |
| 6680 | Type *PtrTy = ToVectorTy(Ptr->getType(), VF); |
| 6681 | |
| 6682 | // Figure out whether the access is strided and get the stride value |
| 6683 | // if it's known in compile time |
| 6684 | const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, PSE, TheLoop); |
| 6685 | |
| 6686 | // Get the cost of the scalar memory instruction and address computation. |
| 6687 | InstructionCost Cost = |
| 6688 | VF.getKnownMinValue() * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV); |
| 6689 | |
| 6690 | // Don't pass *I here, since it is scalar but will actually be part of a |
| 6691 | // vectorized loop where the user of it is a vectorized instruction. |
| 6692 | const Align Alignment = getLoadStoreAlignment(I); |
| 6693 | Cost += VF.getKnownMinValue() * |
| 6694 | TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment, |
| 6695 | AS, TTI::TCK_RecipThroughput); |
| 6696 | |
| 6697 | // Get the overhead of the extractelement and insertelement instructions |
| 6698 | // we might create due to scalarization. |
| 6699 | Cost += getScalarizationOverhead(I, VF); |
| 6700 | |
| 6701 | // If we have a predicated store, it may not be executed for each vector |
| 6702 | // lane. Scale the cost by the probability of executing the predicated |
| 6703 | // block. |
| 6704 | if (isPredicatedInst(I)) { |
| 6705 | Cost /= getReciprocalPredBlockProb(); |
| 6706 | |
| 6707 | if (useEmulatedMaskMemRefHack(I)) |
| 6708 | // Artificially setting to a high enough value to practically disable |
| 6709 | // vectorization with such operations. |
| 6710 | Cost = 3000000; |
| 6711 | } |
| 6712 | |
| 6713 | return Cost; |
| 6714 | } |
| 6715 | |
| 6716 | InstructionCost |
| 6717 | LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I, |
| 6718 | ElementCount VF) { |
| 6719 | Type *ValTy = getMemInstValueType(I); |
| 6720 | auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF)); |
| 6721 | Value *Ptr = getLoadStorePointerOperand(I); |
| 6722 | unsigned AS = getLoadStoreAddressSpace(I); |
| 6723 | int ConsecutiveStride = Legal->isConsecutivePtr(Ptr); |
| 6724 | enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; |
| 6725 | |
| 6726 | assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) && |
| 6727 | "Stride should be 1 or -1 for consecutive memory access" ); |
| 6728 | const Align Alignment = getLoadStoreAlignment(I); |
| 6729 | InstructionCost Cost = 0; |
| 6730 | if (Legal->isMaskRequired(I)) |
| 6731 | Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS, |
| 6732 | CostKind); |
| 6733 | else |
| 6734 | Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS, |
| 6735 | CostKind, I); |
| 6736 | |
| 6737 | bool Reverse = ConsecutiveStride < 0; |
| 6738 | if (Reverse) |
| 6739 | Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0); |
| 6740 | return Cost; |
| 6741 | } |
| 6742 | |
| 6743 | InstructionCost |
| 6744 | LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I, |
| 6745 | ElementCount VF) { |
| 6746 | assert(Legal->isUniformMemOp(*I)); |
| 6747 | |
| 6748 | Type *ValTy = getMemInstValueType(I); |
| 6749 | auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF)); |
| 6750 | const Align Alignment = getLoadStoreAlignment(I); |
| 6751 | unsigned AS = getLoadStoreAddressSpace(I); |
| 6752 | enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; |
| 6753 | if (isa<LoadInst>(I)) { |
| 6754 | return TTI.getAddressComputationCost(ValTy) + |
| 6755 | TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS, |
| 6756 | CostKind) + |
| 6757 | TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy); |
| 6758 | } |
| 6759 | StoreInst *SI = cast<StoreInst>(I); |
| 6760 | |
| 6761 | bool isLoopInvariantStoreValue = Legal->isUniform(SI->getValueOperand()); |
| 6762 | return TTI.getAddressComputationCost(ValTy) + |
| 6763 | TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS, |
| 6764 | CostKind) + |
| 6765 | (isLoopInvariantStoreValue |
| 6766 | ? 0 |
| 6767 | : TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy, |
| 6768 | VF.getKnownMinValue() - 1)); |
| 6769 | } |
| 6770 | |
| 6771 | InstructionCost |
| 6772 | LoopVectorizationCostModel::getGatherScatterCost(Instruction *I, |
| 6773 | ElementCount VF) { |
| 6774 | Type *ValTy = getMemInstValueType(I); |
| 6775 | auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF)); |
| 6776 | const Align Alignment = getLoadStoreAlignment(I); |
| 6777 | const Value *Ptr = getLoadStorePointerOperand(I); |
| 6778 | |
| 6779 | return TTI.getAddressComputationCost(VectorTy) + |
| 6780 | TTI.getGatherScatterOpCost( |
| 6781 | I->getOpcode(), VectorTy, Ptr, Legal->isMaskRequired(I), Alignment, |
| 6782 | TargetTransformInfo::TCK_RecipThroughput, I); |
| 6783 | } |
| 6784 | |
| 6785 | InstructionCost |
| 6786 | LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I, |
| 6787 | ElementCount VF) { |
| 6788 | Type *ValTy = getMemInstValueType(I); |
| 6789 | auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF)); |
| 6790 | unsigned AS = getLoadStoreAddressSpace(I); |
| 6791 | |
| 6792 | auto Group = getInterleavedAccessGroup(I); |
| 6793 | assert(Group && "Fail to get an interleaved access group." ); |
| 6794 | |
| 6795 | unsigned InterleaveFactor = Group->getFactor(); |
| 6796 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 6797 | auto *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor); |
| 6798 | |
| 6799 | // Holds the indices of existing members in an interleaved load group. |
| 6800 | // An interleaved store group doesn't need this as it doesn't allow gaps. |
| 6801 | SmallVector<unsigned, 4> Indices; |
| 6802 | if (isa<LoadInst>(I)) { |
| 6803 | for (unsigned i = 0; i < InterleaveFactor; i++) |
| 6804 | if (Group->getMember(i)) |
| 6805 | Indices.push_back(i); |
| 6806 | } |
| 6807 | |
| 6808 | // Calculate the cost of the whole interleaved group. |
| 6809 | bool UseMaskForGaps = |
| 6810 | Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed(); |
| 6811 | InstructionCost Cost = TTI.getInterleavedMemoryOpCost( |
| 6812 | I->getOpcode(), WideVecTy, Group->getFactor(), Indices, Group->getAlign(), |
| 6813 | AS, TTI::TCK_RecipThroughput, Legal->isMaskRequired(I), UseMaskForGaps); |
| 6814 | |
| 6815 | if (Group->isReverse()) { |
| 6816 | // TODO: Add support for reversed masked interleaved access. |
| 6817 | assert(!Legal->isMaskRequired(I) && |
| 6818 | "Reverse masked interleaved access not supported." ); |
| 6819 | Cost += Group->getNumMembers() * |
| 6820 | TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0); |
| 6821 | } |
| 6822 | return Cost; |
| 6823 | } |
| 6824 | |
| 6825 | InstructionCost LoopVectorizationCostModel::getReductionPatternCost( |
| 6826 | Instruction *I, ElementCount VF, Type *Ty, TTI::TargetCostKind CostKind) { |
| 6827 | // Early exit for no inloop reductions |
| 6828 | if (InLoopReductionChains.empty() || VF.isScalar() || !isa<VectorType>(Ty)) |
| 6829 | return InstructionCost::getInvalid(); |
| 6830 | auto *VectorTy = cast<VectorType>(Ty); |
| 6831 | |
| 6832 | // We are looking for a pattern of, and finding the minimal acceptable cost: |
| 6833 | // reduce(mul(ext(A), ext(B))) or |
| 6834 | // reduce(mul(A, B)) or |
| 6835 | // reduce(ext(A)) or |
| 6836 | // reduce(A). |
| 6837 | // The basic idea is that we walk down the tree to do that, finding the root |
| 6838 | // reduction instruction in InLoopReductionImmediateChains. From there we find |
| 6839 | // the pattern of mul/ext and test the cost of the entire pattern vs the cost |
| 6840 | // of the components. If the reduction cost is lower then we return it for the |
| 6841 | // reduction instruction and 0 for the other instructions in the pattern. If |
| 6842 | // it is not we return an invalid cost specifying the orignal cost method |
| 6843 | // should be used. |
| 6844 | Instruction *RetI = I; |
| 6845 | if ((RetI->getOpcode() == Instruction::SExt || |
| 6846 | RetI->getOpcode() == Instruction::ZExt)) { |
| 6847 | if (!RetI->hasOneUser()) |
| 6848 | return InstructionCost::getInvalid(); |
| 6849 | RetI = RetI->user_back(); |
| 6850 | } |
| 6851 | if (RetI->getOpcode() == Instruction::Mul && |
| 6852 | RetI->user_back()->getOpcode() == Instruction::Add) { |
| 6853 | if (!RetI->hasOneUser()) |
| 6854 | return InstructionCost::getInvalid(); |
| 6855 | RetI = RetI->user_back(); |
| 6856 | } |
| 6857 | |
| 6858 | // Test if the found instruction is a reduction, and if not return an invalid |
| 6859 | // cost specifying the parent to use the original cost modelling. |
| 6860 | if (!InLoopReductionImmediateChains.count(RetI)) |
| 6861 | return InstructionCost::getInvalid(); |
| 6862 | |
| 6863 | // Find the reduction this chain is a part of and calculate the basic cost of |
| 6864 | // the reduction on its own. |
| 6865 | Instruction *LastChain = InLoopReductionImmediateChains[RetI]; |
| 6866 | Instruction *ReductionPhi = LastChain; |
| 6867 | while (!isa<PHINode>(ReductionPhi)) |
| 6868 | ReductionPhi = InLoopReductionImmediateChains[ReductionPhi]; |
| 6869 | |
| 6870 | RecurrenceDescriptor RdxDesc = |
| 6871 | Legal->getReductionVars()[cast<PHINode>(ReductionPhi)]; |
| 6872 | unsigned BaseCost = TTI.getArithmeticReductionCost(RdxDesc.getOpcode(), |
| 6873 | VectorTy, false, CostKind); |
| 6874 | |
| 6875 | // Get the operand that was not the reduction chain and match it to one of the |
| 6876 | // patterns, returning the better cost if it is found. |
| 6877 | Instruction *RedOp = RetI->getOperand(1) == LastChain |
| 6878 | ? dyn_cast<Instruction>(RetI->getOperand(0)) |
| 6879 | : dyn_cast<Instruction>(RetI->getOperand(1)); |
| 6880 | |
| 6881 | VectorTy = VectorType::get(I->getOperand(0)->getType(), VectorTy); |
| 6882 | |
| 6883 | if (RedOp && (isa<SExtInst>(RedOp) || isa<ZExtInst>(RedOp)) && |
| 6884 | !TheLoop->isLoopInvariant(RedOp)) { |
| 6885 | bool IsUnsigned = isa<ZExtInst>(RedOp); |
| 6886 | auto *ExtType = VectorType::get(RedOp->getOperand(0)->getType(), VectorTy); |
| 6887 | InstructionCost RedCost = TTI.getExtendedAddReductionCost( |
| 6888 | /*IsMLA=*/false, IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, |
| 6889 | CostKind); |
| 6890 | |
| 6891 | unsigned ExtCost = |
| 6892 | TTI.getCastInstrCost(RedOp->getOpcode(), VectorTy, ExtType, |
| 6893 | TTI::CastContextHint::None, CostKind, RedOp); |
| 6894 | if (RedCost.isValid() && RedCost < BaseCost + ExtCost) |
| 6895 | return I == RetI ? *RedCost.getValue() : 0; |
| 6896 | } else if (RedOp && RedOp->getOpcode() == Instruction::Mul) { |
| 6897 | Instruction *Mul = RedOp; |
| 6898 | Instruction *Op0 = dyn_cast<Instruction>(Mul->getOperand(0)); |
| 6899 | Instruction *Op1 = dyn_cast<Instruction>(Mul->getOperand(1)); |
| 6900 | if (Op0 && Op1 && (isa<SExtInst>(Op0) || isa<ZExtInst>(Op0)) && |
| 6901 | Op0->getOpcode() == Op1->getOpcode() && |
| 6902 | Op0->getOperand(0)->getType() == Op1->getOperand(0)->getType() && |
| 6903 | !TheLoop->isLoopInvariant(Op0) && !TheLoop->isLoopInvariant(Op1)) { |
| 6904 | bool IsUnsigned = isa<ZExtInst>(Op0); |
| 6905 | auto *ExtType = VectorType::get(Op0->getOperand(0)->getType(), VectorTy); |
| 6906 | // reduce(mul(ext, ext)) |
| 6907 | unsigned ExtCost = |
| 6908 | TTI.getCastInstrCost(Op0->getOpcode(), VectorTy, ExtType, |
| 6909 | TTI::CastContextHint::None, CostKind, Op0); |
| 6910 | unsigned MulCost = |
| 6911 | TTI.getArithmeticInstrCost(Mul->getOpcode(), VectorTy, CostKind); |
| 6912 | |
| 6913 | InstructionCost RedCost = TTI.getExtendedAddReductionCost( |
| 6914 | /*IsMLA=*/true, IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, |
| 6915 | CostKind); |
| 6916 | |
| 6917 | if (RedCost.isValid() && RedCost < ExtCost * 2 + MulCost + BaseCost) |
| 6918 | return I == RetI ? *RedCost.getValue() : 0; |
| 6919 | } else { |
| 6920 | unsigned MulCost = |
| 6921 | TTI.getArithmeticInstrCost(Mul->getOpcode(), VectorTy, CostKind); |
| 6922 | |
| 6923 | InstructionCost RedCost = TTI.getExtendedAddReductionCost( |
| 6924 | /*IsMLA=*/true, true, RdxDesc.getRecurrenceType(), VectorTy, |
| 6925 | CostKind); |
| 6926 | |
| 6927 | if (RedCost.isValid() && RedCost < MulCost + BaseCost) |
| 6928 | return I == RetI ? *RedCost.getValue() : 0; |
| 6929 | } |
| 6930 | } |
| 6931 | |
| 6932 | return I == RetI ? BaseCost : InstructionCost::getInvalid(); |
| 6933 | } |
| 6934 | |
| 6935 | InstructionCost |
| 6936 | LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I, |
| 6937 | ElementCount VF) { |
| 6938 | // Calculate scalar cost only. Vectorization cost should be ready at this |
| 6939 | // moment. |
| 6940 | if (VF.isScalar()) { |
| 6941 | Type *ValTy = getMemInstValueType(I); |
| 6942 | const Align Alignment = getLoadStoreAlignment(I); |
| 6943 | unsigned AS = getLoadStoreAddressSpace(I); |
| 6944 | |
| 6945 | return TTI.getAddressComputationCost(ValTy) + |
| 6946 | TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS, |
| 6947 | TTI::TCK_RecipThroughput, I); |
| 6948 | } |
| 6949 | return getWideningCost(I, VF); |
| 6950 | } |
| 6951 | |
| 6952 | LoopVectorizationCostModel::VectorizationCostTy |
| 6953 | LoopVectorizationCostModel::getInstructionCost(Instruction *I, |
| 6954 | ElementCount VF) { |
| 6955 | // If we know that this instruction will remain uniform, check the cost of |
| 6956 | // the scalar version. |
| 6957 | if (isUniformAfterVectorization(I, VF)) |
| 6958 | VF = ElementCount::getFixed(1); |
| 6959 | |
| 6960 | if (VF.isVector() && isProfitableToScalarize(I, VF)) |
| 6961 | return VectorizationCostTy(InstsToScalarize[VF][I], false); |
| 6962 | |
| 6963 | // Forced scalars do not have any scalarization overhead. |
| 6964 | auto ForcedScalar = ForcedScalars.find(VF); |
| 6965 | if (VF.isVector() && ForcedScalar != ForcedScalars.end()) { |
| 6966 | auto InstSet = ForcedScalar->second; |
| 6967 | if (InstSet.count(I)) |
| 6968 | return VectorizationCostTy( |
| 6969 | (getInstructionCost(I, ElementCount::getFixed(1)).first * |
| 6970 | VF.getKnownMinValue()), |
| 6971 | false); |
| 6972 | } |
| 6973 | |
| 6974 | Type *VectorTy; |
| 6975 | InstructionCost C = getInstructionCost(I, VF, VectorTy); |
| 6976 | |
| 6977 | bool TypeNotScalarized = |
| 6978 | VF.isVector() && VectorTy->isVectorTy() && |
| 6979 | TTI.getNumberOfParts(VectorTy) < VF.getKnownMinValue(); |
| 6980 | return VectorizationCostTy(C, TypeNotScalarized); |
| 6981 | } |
| 6982 | |
| 6983 | InstructionCost |
| 6984 | LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I, |
| 6985 | ElementCount VF) { |
| 6986 | |
| 6987 | assert(!VF.isScalable() && |
| 6988 | "cannot compute scalarization overhead for scalable vectorization" ); |
| 6989 | if (VF.isScalar()) |
| 6990 | return 0; |
| 6991 | |
| 6992 | InstructionCost Cost = 0; |
| 6993 | Type *RetTy = ToVectorTy(I->getType(), VF); |
| 6994 | if (!RetTy->isVoidTy() && |
| 6995 | (!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore())) |
| 6996 | Cost += TTI.getScalarizationOverhead( |
| 6997 | cast<VectorType>(RetTy), APInt::getAllOnesValue(VF.getKnownMinValue()), |
| 6998 | true, false); |
| 6999 | |
| 7000 | // Some targets keep addresses scalar. |
| 7001 | if (isa<LoadInst>(I) && !TTI.prefersVectorizedAddressing()) |
| 7002 | return Cost; |
| 7003 | |
| 7004 | // Some targets support efficient element stores. |
| 7005 | if (isa<StoreInst>(I) && TTI.supportsEfficientVectorElementLoadStore()) |
| 7006 | return Cost; |
| 7007 | |
| 7008 | // Collect operands to consider. |
| 7009 | CallInst *CI = dyn_cast<CallInst>(I); |
| 7010 | Instruction::op_range Ops = CI ? CI->arg_operands() : I->operands(); |
| 7011 | |
| 7012 | // Skip operands that do not require extraction/scalarization and do not incur |
| 7013 | // any overhead. |
| 7014 | return Cost + TTI.getOperandsScalarizationOverhead( |
| 7015 | filterExtractingOperands(Ops, VF), VF.getKnownMinValue()); |
| 7016 | } |
| 7017 | |
| 7018 | void LoopVectorizationCostModel::setCostBasedWideningDecision(ElementCount VF) { |
| 7019 | if (VF.isScalar()) |
| 7020 | return; |
| 7021 | NumPredStores = 0; |
| 7022 | for (BasicBlock *BB : TheLoop->blocks()) { |
| 7023 | // For each instruction in the old loop. |
| 7024 | for (Instruction &I : *BB) { |
| 7025 | Value *Ptr = getLoadStorePointerOperand(&I); |
| 7026 | if (!Ptr) |
| 7027 | continue; |
| 7028 | |
| 7029 | // TODO: We should generate better code and update the cost model for |
| 7030 | // predicated uniform stores. Today they are treated as any other |
| 7031 | // predicated store (see added test cases in |
| 7032 | // invariant-store-vectorization.ll). |
| 7033 | if (isa<StoreInst>(&I) && isScalarWithPredication(&I)) |
| 7034 | NumPredStores++; |
| 7035 | |
| 7036 | if (Legal->isUniformMemOp(I)) { |
| 7037 | // TODO: Avoid replicating loads and stores instead of |
| 7038 | // relying on instcombine to remove them. |
| 7039 | // Load: Scalar load + broadcast |
| 7040 | // Store: Scalar store + isLoopInvariantStoreValue ? 0 : extract |
| 7041 | InstructionCost Cost = getUniformMemOpCost(&I, VF); |
| 7042 | setWideningDecision(&I, VF, CM_Scalarize, Cost); |
| 7043 | continue; |
| 7044 | } |
| 7045 | |
| 7046 | // We assume that widening is the best solution when possible. |
| 7047 | if (memoryInstructionCanBeWidened(&I, VF)) { |
| 7048 | InstructionCost Cost = getConsecutiveMemOpCost(&I, VF); |
| 7049 | int ConsecutiveStride = |
| 7050 | Legal->isConsecutivePtr(getLoadStorePointerOperand(&I)); |
| 7051 | assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) && |
| 7052 | "Expected consecutive stride." ); |
| 7053 | InstWidening Decision = |
| 7054 | ConsecutiveStride == 1 ? CM_Widen : CM_Widen_Reverse; |
| 7055 | setWideningDecision(&I, VF, Decision, Cost); |
| 7056 | continue; |
| 7057 | } |
| 7058 | |
| 7059 | // Choose between Interleaving, Gather/Scatter or Scalarization. |
| 7060 | InstructionCost InterleaveCost = std::numeric_limits<int>::max(); |
| 7061 | unsigned NumAccesses = 1; |
| 7062 | if (isAccessInterleaved(&I)) { |
| 7063 | auto Group = getInterleavedAccessGroup(&I); |
| 7064 | assert(Group && "Fail to get an interleaved access group." ); |
| 7065 | |
| 7066 | // Make one decision for the whole group. |
| 7067 | if (getWideningDecision(&I, VF) != CM_Unknown) |
| 7068 | continue; |
| 7069 | |
| 7070 | NumAccesses = Group->getNumMembers(); |
| 7071 | if (interleavedAccessCanBeWidened(&I, VF)) |
| 7072 | InterleaveCost = getInterleaveGroupCost(&I, VF); |
| 7073 | } |
| 7074 | |
| 7075 | InstructionCost GatherScatterCost = |
| 7076 | isLegalGatherOrScatter(&I) |
| 7077 | ? getGatherScatterCost(&I, VF) * NumAccesses |
| 7078 | : std::numeric_limits<int>::max(); |
| 7079 | |
| 7080 | InstructionCost ScalarizationCost = |
| 7081 | getMemInstScalarizationCost(&I, VF) * NumAccesses; |
| 7082 | |
| 7083 | // Choose better solution for the current VF, |
| 7084 | // write down this decision and use it during vectorization. |
| 7085 | InstructionCost Cost; |
| 7086 | InstWidening Decision; |
| 7087 | if (InterleaveCost <= GatherScatterCost && |
| 7088 | InterleaveCost < ScalarizationCost) { |
| 7089 | Decision = CM_Interleave; |
| 7090 | Cost = InterleaveCost; |
| 7091 | } else if (GatherScatterCost < ScalarizationCost) { |
| 7092 | Decision = CM_GatherScatter; |
| 7093 | Cost = GatherScatterCost; |
| 7094 | } else { |
| 7095 | Decision = CM_Scalarize; |
| 7096 | Cost = ScalarizationCost; |
| 7097 | } |
| 7098 | // If the instructions belongs to an interleave group, the whole group |
| 7099 | // receives the same decision. The whole group receives the cost, but |
| 7100 | // the cost will actually be assigned to one instruction. |
| 7101 | if (auto Group = getInterleavedAccessGroup(&I)) |
| 7102 | setWideningDecision(Group, VF, Decision, Cost); |
| 7103 | else |
| 7104 | setWideningDecision(&I, VF, Decision, Cost); |
| 7105 | } |
| 7106 | } |
| 7107 | |
| 7108 | // Make sure that any load of address and any other address computation |
| 7109 | // remains scalar unless there is gather/scatter support. This avoids |
| 7110 | // inevitable extracts into address registers, and also has the benefit of |
| 7111 | // activating LSR more, since that pass can't optimize vectorized |
| 7112 | // addresses. |
| 7113 | if (TTI.prefersVectorizedAddressing()) |
| 7114 | return; |
| 7115 | |
| 7116 | // Start with all scalar pointer uses. |
| 7117 | SmallPtrSet<Instruction *, 8> AddrDefs; |
| 7118 | for (BasicBlock *BB : TheLoop->blocks()) |
| 7119 | for (Instruction &I : *BB) { |
| 7120 | Instruction *PtrDef = |
| 7121 | dyn_cast_or_null<Instruction>(getLoadStorePointerOperand(&I)); |
| 7122 | if (PtrDef && TheLoop->contains(PtrDef) && |
| 7123 | getWideningDecision(&I, VF) != CM_GatherScatter) |
| 7124 | AddrDefs.insert(PtrDef); |
| 7125 | } |
| 7126 | |
| 7127 | // Add all instructions used to generate the addresses. |
| 7128 | SmallVector<Instruction *, 4> Worklist; |
| 7129 | append_range(Worklist, AddrDefs); |
| 7130 | while (!Worklist.empty()) { |
| 7131 | Instruction *I = Worklist.pop_back_val(); |
| 7132 | for (auto &Op : I->operands()) |
| 7133 | if (auto *InstOp = dyn_cast<Instruction>(Op)) |
| 7134 | if ((InstOp->getParent() == I->getParent()) && !isa<PHINode>(InstOp) && |
| 7135 | AddrDefs.insert(InstOp).second) |
| 7136 | Worklist.push_back(InstOp); |
| 7137 | } |
| 7138 | |
| 7139 | for (auto *I : AddrDefs) { |
| 7140 | if (isa<LoadInst>(I)) { |
| 7141 | // Setting the desired widening decision should ideally be handled in |
| 7142 | // by cost functions, but since this involves the task of finding out |
| 7143 | // if the loaded register is involved in an address computation, it is |
| 7144 | // instead changed here when we know this is the case. |
| 7145 | InstWidening Decision = getWideningDecision(I, VF); |
| 7146 | if (Decision == CM_Widen || Decision == CM_Widen_Reverse) |
| 7147 | // Scalarize a widened load of address. |
| 7148 | setWideningDecision( |
| 7149 | I, VF, CM_Scalarize, |
| 7150 | (VF.getKnownMinValue() * |
| 7151 | getMemoryInstructionCost(I, ElementCount::getFixed(1)))); |
| 7152 | else if (auto Group = getInterleavedAccessGroup(I)) { |
| 7153 | // Scalarize an interleave group of address loads. |
| 7154 | for (unsigned I = 0; I < Group->getFactor(); ++I) { |
| 7155 | if (Instruction *Member = Group->getMember(I)) |
| 7156 | setWideningDecision( |
| 7157 | Member, VF, CM_Scalarize, |
| 7158 | (VF.getKnownMinValue() * |
| 7159 | getMemoryInstructionCost(Member, ElementCount::getFixed(1)))); |
| 7160 | } |
| 7161 | } |
| 7162 | } else |
| 7163 | // Make sure I gets scalarized and a cost estimate without |
| 7164 | // scalarization overhead. |
| 7165 | ForcedScalars[VF].insert(I); |
| 7166 | } |
| 7167 | } |
| 7168 | |
| 7169 | InstructionCost |
| 7170 | LoopVectorizationCostModel::getInstructionCost(Instruction *I, ElementCount VF, |
| 7171 | Type *&VectorTy) { |
| 7172 | Type *RetTy = I->getType(); |
| 7173 | if (canTruncateToMinimalBitwidth(I, VF)) |
| 7174 | RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]); |
| 7175 | VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF); |
| 7176 | auto SE = PSE.getSE(); |
| 7177 | TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; |
| 7178 | |
| 7179 | // TODO: We need to estimate the cost of intrinsic calls. |
| 7180 | switch (I->getOpcode()) { |
| 7181 | case Instruction::GetElementPtr: |
| 7182 | // We mark this instruction as zero-cost because the cost of GEPs in |
| 7183 | // vectorized code depends on whether the corresponding memory instruction |
| 7184 | // is scalarized or not. Therefore, we handle GEPs with the memory |
| 7185 | // instruction cost. |
| 7186 | return 0; |
| 7187 | case Instruction::Br: { |
| 7188 | // In cases of scalarized and predicated instructions, there will be VF |
| 7189 | // predicated blocks in the vectorized loop. Each branch around these |
| 7190 | // blocks requires also an extract of its vector compare i1 element. |
| 7191 | bool ScalarPredicatedBB = false; |
| 7192 | BranchInst *BI = cast<BranchInst>(I); |
| 7193 | if (VF.isVector() && BI->isConditional() && |
| 7194 | (PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) || |
| 7195 | PredicatedBBsAfterVectorization.count(BI->getSuccessor(1)))) |
| 7196 | ScalarPredicatedBB = true; |
| 7197 | |
| 7198 | if (ScalarPredicatedBB) { |
| 7199 | // Return cost for branches around scalarized and predicated blocks. |
| 7200 | assert(!VF.isScalable() && "scalable vectors not yet supported." ); |
| 7201 | auto *Vec_i1Ty = |
| 7202 | VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF); |
| 7203 | return (TTI.getScalarizationOverhead( |
| 7204 | Vec_i1Ty, APInt::getAllOnesValue(VF.getKnownMinValue()), |
| 7205 | false, true) + |
| 7206 | (TTI.getCFInstrCost(Instruction::Br, CostKind) * |
| 7207 | VF.getKnownMinValue())); |
| 7208 | } else if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar()) |
| 7209 | // The back-edge branch will remain, as will all scalar branches. |
| 7210 | return TTI.getCFInstrCost(Instruction::Br, CostKind); |
| 7211 | else |
| 7212 | // This branch will be eliminated by if-conversion. |
| 7213 | return 0; |
| 7214 | // Note: We currently assume zero cost for an unconditional branch inside |
| 7215 | // a predicated block since it will become a fall-through, although we |
| 7216 | // may decide in the future to call TTI for all branches. |
| 7217 | } |
| 7218 | case Instruction::PHI: { |
| 7219 | auto *Phi = cast<PHINode>(I); |
| 7220 | |
| 7221 | // First-order recurrences are replaced by vector shuffles inside the loop. |
| 7222 | // NOTE: Don't use ToVectorTy as SK_ExtractSubvector expects a vector type. |
| 7223 | if (VF.isVector() && Legal->isFirstOrderRecurrence(Phi)) |
| 7224 | return TTI.getShuffleCost( |
| 7225 | TargetTransformInfo::SK_ExtractSubvector, cast<VectorType>(VectorTy), |
| 7226 | VF.getKnownMinValue() - 1, FixedVectorType::get(RetTy, 1)); |
| 7227 | |
| 7228 | // Phi nodes in non-header blocks (not inductions, reductions, etc.) are |
| 7229 | // converted into select instructions. We require N - 1 selects per phi |
| 7230 | // node, where N is the number of incoming values. |
| 7231 | if (VF.isVector() && Phi->getParent() != TheLoop->getHeader()) |
| 7232 | return (Phi->getNumIncomingValues() - 1) * |
| 7233 | TTI.getCmpSelInstrCost( |
| 7234 | Instruction::Select, ToVectorTy(Phi->getType(), VF), |
| 7235 | ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF), |
| 7236 | CmpInst::BAD_ICMP_PREDICATE, CostKind); |
| 7237 | |
| 7238 | return TTI.getCFInstrCost(Instruction::PHI, CostKind); |
| 7239 | } |
| 7240 | case Instruction::UDiv: |
| 7241 | case Instruction::SDiv: |
| 7242 | case Instruction::URem: |
| 7243 | case Instruction::SRem: |
| 7244 | // If we have a predicated instruction, it may not be executed for each |
| 7245 | // vector lane. Get the scalarization cost and scale this amount by the |
| 7246 | // probability of executing the predicated block. If the instruction is not |
| 7247 | // predicated, we fall through to the next case. |
| 7248 | if (VF.isVector() && isScalarWithPredication(I)) { |
| 7249 | InstructionCost Cost = 0; |
| 7250 | |
| 7251 | // These instructions have a non-void type, so account for the phi nodes |
| 7252 | // that we will create. This cost is likely to be zero. The phi node |
| 7253 | // cost, if any, should be scaled by the block probability because it |
| 7254 | // models a copy at the end of each predicated block. |
| 7255 | Cost += VF.getKnownMinValue() * |
| 7256 | TTI.getCFInstrCost(Instruction::PHI, CostKind); |
| 7257 | |
| 7258 | // The cost of the non-predicated instruction. |
| 7259 | Cost += VF.getKnownMinValue() * |
| 7260 | TTI.getArithmeticInstrCost(I->getOpcode(), RetTy, CostKind); |
| 7261 | |
| 7262 | // The cost of insertelement and extractelement instructions needed for |
| 7263 | // scalarization. |
| 7264 | Cost += getScalarizationOverhead(I, VF); |
| 7265 | |
| 7266 | // Scale the cost by the probability of executing the predicated blocks. |
| 7267 | // This assumes the predicated block for each vector lane is equally |
| 7268 | // likely. |
| 7269 | return Cost / getReciprocalPredBlockProb(); |
| 7270 | } |
| 7271 | LLVM_FALLTHROUGH; |
| 7272 | case Instruction::Add: |
| 7273 | case Instruction::FAdd: |
| 7274 | case Instruction::Sub: |
| 7275 | case Instruction::FSub: |
| 7276 | case Instruction::Mul: |
| 7277 | case Instruction::FMul: |
| 7278 | case Instruction::FDiv: |
| 7279 | case Instruction::FRem: |
| 7280 | case Instruction::Shl: |
| 7281 | case Instruction::LShr: |
| 7282 | case Instruction::AShr: |
| 7283 | case Instruction::And: |
| 7284 | case Instruction::Or: |
| 7285 | case Instruction::Xor: { |
| 7286 | // Since we will replace the stride by 1 the multiplication should go away. |
| 7287 | if (I->getOpcode() == Instruction::Mul && isStrideMul(I, Legal)) |
| 7288 | return 0; |
| 7289 | |
| 7290 | // Detect reduction patterns |
| 7291 | InstructionCost RedCost; |
| 7292 | if ((RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind)) |
| 7293 | .isValid()) |
| 7294 | return RedCost; |
| 7295 | |
| 7296 | // Certain instructions can be cheaper to vectorize if they have a constant |
| 7297 | // second vector operand. One example of this are shifts on x86. |
| 7298 | Value *Op2 = I->getOperand(1); |
| 7299 | TargetTransformInfo::OperandValueProperties Op2VP; |
| 7300 | TargetTransformInfo::OperandValueKind Op2VK = |
| 7301 | TTI.getOperandInfo(Op2, Op2VP); |
| 7302 | if (Op2VK == TargetTransformInfo::OK_AnyValue && Legal->isUniform(Op2)) |
| 7303 | Op2VK = TargetTransformInfo::OK_UniformValue; |
| 7304 | |
| 7305 | SmallVector<const Value *, 4> Operands(I->operand_values()); |
| 7306 | unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; |
| 7307 | return N * TTI.getArithmeticInstrCost( |
| 7308 | I->getOpcode(), VectorTy, CostKind, |
| 7309 | TargetTransformInfo::OK_AnyValue, |
| 7310 | Op2VK, TargetTransformInfo::OP_None, Op2VP, Operands, I); |
| 7311 | } |
| 7312 | case Instruction::FNeg: { |
| 7313 | assert(!VF.isScalable() && "VF is assumed to be non scalable." ); |
| 7314 | unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; |
| 7315 | return N * TTI.getArithmeticInstrCost( |
| 7316 | I->getOpcode(), VectorTy, CostKind, |
| 7317 | TargetTransformInfo::OK_AnyValue, |
| 7318 | TargetTransformInfo::OK_AnyValue, |
| 7319 | TargetTransformInfo::OP_None, TargetTransformInfo::OP_None, |
| 7320 | I->getOperand(0), I); |
| 7321 | } |
| 7322 | case Instruction::Select: { |
| 7323 | SelectInst *SI = cast<SelectInst>(I); |
| 7324 | const SCEV *CondSCEV = SE->getSCEV(SI->getCondition()); |
| 7325 | bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop)); |
| 7326 | Type *CondTy = SI->getCondition()->getType(); |
| 7327 | if (!ScalarCond) |
| 7328 | CondTy = VectorType::get(CondTy, VF); |
| 7329 | return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy, |
| 7330 | CmpInst::BAD_ICMP_PREDICATE, CostKind, I); |
| 7331 | } |
| 7332 | case Instruction::ICmp: |
| 7333 | case Instruction::FCmp: { |
| 7334 | Type *ValTy = I->getOperand(0)->getType(); |
| 7335 | Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0)); |
| 7336 | if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF)) |
| 7337 | ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]); |
| 7338 | VectorTy = ToVectorTy(ValTy, VF); |
| 7339 | return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr, |
| 7340 | CmpInst::BAD_ICMP_PREDICATE, CostKind, I); |
| 7341 | } |
| 7342 | case Instruction::Store: |
| 7343 | case Instruction::Load: { |
| 7344 | ElementCount Width = VF; |
| 7345 | if (Width.isVector()) { |
| 7346 | InstWidening Decision = getWideningDecision(I, Width); |
| 7347 | assert(Decision != CM_Unknown && |
| 7348 | "CM decision should be taken at this point" ); |
| 7349 | if (Decision == CM_Scalarize) |
| 7350 | Width = ElementCount::getFixed(1); |
| 7351 | } |
| 7352 | VectorTy = ToVectorTy(getMemInstValueType(I), Width); |
| 7353 | return getMemoryInstructionCost(I, VF); |
| 7354 | } |
| 7355 | case Instruction::ZExt: |
| 7356 | case Instruction::SExt: |
| 7357 | case Instruction::FPToUI: |
| 7358 | case Instruction::FPToSI: |
| 7359 | case Instruction::FPExt: |
| 7360 | case Instruction::PtrToInt: |
| 7361 | case Instruction::IntToPtr: |
| 7362 | case Instruction::SIToFP: |
| 7363 | case Instruction::UIToFP: |
| 7364 | case Instruction::Trunc: |
| 7365 | case Instruction::FPTrunc: |
| 7366 | case Instruction::BitCast: { |
| 7367 | // Computes the CastContextHint from a Load/Store instruction. |
| 7368 | auto ComputeCCH = [&](Instruction *I) -> TTI::CastContextHint { |
| 7369 | assert((isa<LoadInst>(I) || isa<StoreInst>(I)) && |
| 7370 | "Expected a load or a store!" ); |
| 7371 | |
| 7372 | if (VF.isScalar() || !TheLoop->contains(I)) |
| 7373 | return TTI::CastContextHint::Normal; |
| 7374 | |
| 7375 | switch (getWideningDecision(I, VF)) { |
| 7376 | case LoopVectorizationCostModel::CM_GatherScatter: |
| 7377 | return TTI::CastContextHint::GatherScatter; |
| 7378 | case LoopVectorizationCostModel::CM_Interleave: |
| 7379 | return TTI::CastContextHint::Interleave; |
| 7380 | case LoopVectorizationCostModel::CM_Scalarize: |
| 7381 | case LoopVectorizationCostModel::CM_Widen: |
| 7382 | return Legal->isMaskRequired(I) ? TTI::CastContextHint::Masked |
| 7383 | : TTI::CastContextHint::Normal; |
| 7384 | case LoopVectorizationCostModel::CM_Widen_Reverse: |
| 7385 | return TTI::CastContextHint::Reversed; |
| 7386 | case LoopVectorizationCostModel::CM_Unknown: |
| 7387 | llvm_unreachable("Instr did not go through cost modelling?" ); |
| 7388 | } |
| 7389 | |
| 7390 | llvm_unreachable("Unhandled case!" ); |
| 7391 | }; |
| 7392 | |
| 7393 | unsigned Opcode = I->getOpcode(); |
| 7394 | TTI::CastContextHint CCH = TTI::CastContextHint::None; |
| 7395 | // For Trunc, the context is the only user, which must be a StoreInst. |
| 7396 | if (Opcode == Instruction::Trunc || Opcode == Instruction::FPTrunc) { |
| 7397 | if (I->hasOneUse()) |
| 7398 | if (StoreInst *Store = dyn_cast<StoreInst>(*I->user_begin())) |
| 7399 | CCH = ComputeCCH(Store); |
| 7400 | } |
| 7401 | // For Z/Sext, the context is the operand, which must be a LoadInst. |
| 7402 | else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt || |
| 7403 | Opcode == Instruction::FPExt) { |
| 7404 | if (LoadInst *Load = dyn_cast<LoadInst>(I->getOperand(0))) |
| 7405 | CCH = ComputeCCH(Load); |
| 7406 | } |
| 7407 | |
| 7408 | // We optimize the truncation of induction variables having constant |
| 7409 | // integer steps. The cost of these truncations is the same as the scalar |
| 7410 | // operation. |
| 7411 | if (isOptimizableIVTruncate(I, VF)) { |
| 7412 | auto *Trunc = cast<TruncInst>(I); |
| 7413 | return TTI.getCastInstrCost(Instruction::Trunc, Trunc->getDestTy(), |
| 7414 | Trunc->getSrcTy(), CCH, CostKind, Trunc); |
| 7415 | } |
| 7416 | |
| 7417 | // Detect reduction patterns |
| 7418 | InstructionCost RedCost; |
| 7419 | if ((RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind)) |
| 7420 | .isValid()) |
| 7421 | return RedCost; |
| 7422 | |
| 7423 | Type *SrcScalarTy = I->getOperand(0)->getType(); |
| 7424 | Type *SrcVecTy = |
| 7425 | VectorTy->isVectorTy() ? ToVectorTy(SrcScalarTy, VF) : SrcScalarTy; |
| 7426 | if (canTruncateToMinimalBitwidth(I, VF)) { |
| 7427 | // This cast is going to be shrunk. This may remove the cast or it might |
| 7428 | // turn it into slightly different cast. For example, if MinBW == 16, |
| 7429 | // "zext i8 %1 to i32" becomes "zext i8 %1 to i16". |
| 7430 | // |
| 7431 | // Calculate the modified src and dest types. |
| 7432 | Type *MinVecTy = VectorTy; |
| 7433 | if (Opcode == Instruction::Trunc) { |
| 7434 | SrcVecTy = smallestIntegerVectorType(SrcVecTy, MinVecTy); |
| 7435 | VectorTy = |
| 7436 | largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy); |
| 7437 | } else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt) { |
| 7438 | SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy); |
| 7439 | VectorTy = |
| 7440 | smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy); |
| 7441 | } |
| 7442 | } |
| 7443 | |
| 7444 | assert(!VF.isScalable() && "VF is assumed to be non scalable" ); |
| 7445 | unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; |
| 7446 | return N * |
| 7447 | TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH, CostKind, I); |
| 7448 | } |
| 7449 | case Instruction::Call: { |
| 7450 | bool NeedToScalarize; |
| 7451 | CallInst *CI = cast<CallInst>(I); |
| 7452 | InstructionCost CallCost = getVectorCallCost(CI, VF, NeedToScalarize); |
| 7453 | if (getVectorIntrinsicIDForCall(CI, TLI)) { |
| 7454 | InstructionCost IntrinsicCost = getVectorIntrinsicCost(CI, VF); |
| 7455 | return std::min(CallCost, IntrinsicCost); |
| 7456 | } |
| 7457 | return CallCost; |
| 7458 | } |
| 7459 | case Instruction::ExtractValue: |
| 7460 | return TTI.getInstructionCost(I, TTI::TCK_RecipThroughput); |
| 7461 | default: |
| 7462 | // The cost of executing VF copies of the scalar instruction. This opcode |
| 7463 | // is unknown. Assume that it is the same as 'mul'. |
| 7464 | return VF.getKnownMinValue() * TTI.getArithmeticInstrCost( |
| 7465 | Instruction::Mul, VectorTy, CostKind) + |
| 7466 | getScalarizationOverhead(I, VF); |
| 7467 | } // end of switch. |
| 7468 | } |
| 7469 | |
| 7470 | char LoopVectorize::ID = 0; |
| 7471 | |
| 7472 | static const char lv_name[] = "Loop Vectorization" ; |
| 7473 | |
| 7474 | INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false) |
| 7475 | INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass) |
| 7476 | INITIALIZE_PASS_DEPENDENCY(BasicAAWrapperPass) |
| 7477 | INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass) |
| 7478 | INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass) |
| 7479 | INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker) |
| 7480 | INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass) |
| 7481 | INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass) |
| 7482 | INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass) |
| 7483 | INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass) |
| 7484 | INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis) |
| 7485 | INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass) |
| 7486 | INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass) |
| 7487 | INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass) |
| 7488 | INITIALIZE_PASS_DEPENDENCY(InjectTLIMappingsLegacy) |
| 7489 | INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false) |
| 7490 | |
| 7491 | namespace llvm { |
| 7492 | |
| 7493 | Pass *createLoopVectorizePass() { return new LoopVectorize(); } |
| 7494 | |
| 7495 | Pass *createLoopVectorizePass(bool InterleaveOnlyWhenForced, |
| 7496 | bool VectorizeOnlyWhenForced) { |
| 7497 | return new LoopVectorize(InterleaveOnlyWhenForced, VectorizeOnlyWhenForced); |
| 7498 | } |
| 7499 | |
| 7500 | } // end namespace llvm |
| 7501 | |
| 7502 | bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) { |
| 7503 | // Check if the pointer operand of a load or store instruction is |
| 7504 | // consecutive. |
| 7505 | if (auto *Ptr = getLoadStorePointerOperand(Inst)) |
| 7506 | return Legal->isConsecutivePtr(Ptr); |
| 7507 | return false; |
| 7508 | } |
| 7509 | |
| 7510 | void LoopVectorizationCostModel::collectValuesToIgnore() { |
| 7511 | // Ignore ephemeral values. |
| 7512 | CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore); |
| 7513 | |
| 7514 | // Ignore type-promoting instructions we identified during reduction |
| 7515 | // detection. |
| 7516 | for (auto &Reduction : Legal->getReductionVars()) { |
| 7517 | RecurrenceDescriptor &RedDes = Reduction.second; |
| 7518 | const SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts(); |
| 7519 | VecValuesToIgnore.insert(Casts.begin(), Casts.end()); |
| 7520 | } |
| 7521 | // Ignore type-casting instructions we identified during induction |
| 7522 | // detection. |
| 7523 | for (auto &Induction : Legal->getInductionVars()) { |
| 7524 | InductionDescriptor &IndDes = Induction.second; |
| 7525 | const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts(); |
| 7526 | VecValuesToIgnore.insert(Casts.begin(), Casts.end()); |
| 7527 | } |
| 7528 | } |
| 7529 | |
| 7530 | void LoopVectorizationCostModel::collectInLoopReductions() { |
| 7531 | for (auto &Reduction : Legal->getReductionVars()) { |
| 7532 | PHINode *Phi = Reduction.first; |
| 7533 | RecurrenceDescriptor &RdxDesc = Reduction.second; |
| 7534 | |
| 7535 | // We don't collect reductions that are type promoted (yet). |
| 7536 | if (RdxDesc.getRecurrenceType() != Phi->getType()) |
| 7537 | continue; |
| 7538 | |
| 7539 | // If the target would prefer this reduction to happen "in-loop", then we |
| 7540 | // want to record it as such. |
| 7541 | unsigned Opcode = RdxDesc.getOpcode(); |
| 7542 | if (!PreferInLoopReductions && |
| 7543 | !TTI.preferInLoopReduction(Opcode, Phi->getType(), |
| 7544 | TargetTransformInfo::ReductionFlags())) |
| 7545 | continue; |
| 7546 | |
| 7547 | // Check that we can correctly put the reductions into the loop, by |
| 7548 | // finding the chain of operations that leads from the phi to the loop |
| 7549 | // exit value. |
| 7550 | SmallVector<Instruction *, 4> ReductionOperations = |
| 7551 | RdxDesc.getReductionOpChain(Phi, TheLoop); |
| 7552 | bool InLoop = !ReductionOperations.empty(); |
| 7553 | if (InLoop) { |
| 7554 | InLoopReductionChains[Phi] = ReductionOperations; |
| 7555 | // Add the elements to InLoopReductionImmediateChains for cost modelling. |
| 7556 | Instruction *LastChain = Phi; |
| 7557 | for (auto *I : ReductionOperations) { |
| 7558 | InLoopReductionImmediateChains[I] = LastChain; |
| 7559 | LastChain = I; |
| 7560 | } |
| 7561 | } |
| 7562 | LLVM_DEBUG(dbgs() << "LV: Using " << (InLoop ? "inloop" : "out of loop" ) |
| 7563 | << " reduction for phi: " << *Phi << "\n" ); |
| 7564 | } |
| 7565 | } |
| 7566 | |
| 7567 | // TODO: we could return a pair of values that specify the max VF and |
| 7568 | // min VF, to be used in `buildVPlans(MinVF, MaxVF)` instead of |
| 7569 | // `buildVPlans(VF, VF)`. We cannot do it because VPLAN at the moment |
| 7570 | // doesn't have a cost model that can choose which plan to execute if |
| 7571 | // more than one is generated. |
| 7572 | static unsigned determineVPlanVF(const unsigned WidestVectorRegBits, |
| 7573 | LoopVectorizationCostModel &CM) { |
| 7574 | unsigned WidestType; |
| 7575 | std::tie(std::ignore, WidestType) = CM.getSmallestAndWidestTypes(); |
| 7576 | return WidestVectorRegBits / WidestType; |
| 7577 | } |
| 7578 | |
| 7579 | VectorizationFactor |
| 7580 | LoopVectorizationPlanner::planInVPlanNativePath(ElementCount UserVF) { |
| 7581 | assert(!UserVF.isScalable() && "scalable vectors not yet supported" ); |
| 7582 | ElementCount VF = UserVF; |
| 7583 | // Outer loop handling: They may require CFG and instruction level |
| 7584 | // transformations before even evaluating whether vectorization is profitable. |
| 7585 | // Since we cannot modify the incoming IR, we need to build VPlan upfront in |
| 7586 | // the vectorization pipeline. |
| 7587 | if (!OrigLoop->isInnermost()) { |
| 7588 | // If the user doesn't provide a vectorization factor, determine a |
| 7589 | // reasonable one. |
| 7590 | if (UserVF.isZero()) { |
| 7591 | VF = ElementCount::getFixed( |
| 7592 | determineVPlanVF(TTI->getRegisterBitWidth(true /* Vector*/), CM)); |
| 7593 | LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n" ); |
| 7594 | |
| 7595 | // Make sure we have a VF > 1 for stress testing. |
| 7596 | if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) { |
| 7597 | LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: " |
| 7598 | << "overriding computed VF.\n" ); |
| 7599 | VF = ElementCount::getFixed(4); |
| 7600 | } |
| 7601 | } |
| 7602 | assert(EnableVPlanNativePath && "VPlan-native path is not enabled." ); |
| 7603 | assert(isPowerOf2_32(VF.getKnownMinValue()) && |
| 7604 | "VF needs to be a power of two" ); |
| 7605 | LLVM_DEBUG(dbgs() << "LV: Using " << (!UserVF.isZero() ? "user " : "" ) |
| 7606 | << "VF " << VF << " to build VPlans.\n" ); |
| 7607 | buildVPlans(VF, VF); |
| 7608 | |
| 7609 | // For VPlan build stress testing, we bail out after VPlan construction. |
| 7610 | if (VPlanBuildStressTest) |
| 7611 | return VectorizationFactor::Disabled(); |
| 7612 | |
| 7613 | return {VF, 0 /*Cost*/}; |
| 7614 | } |
| 7615 | |
| 7616 | LLVM_DEBUG( |
| 7617 | dbgs() << "LV: Not vectorizing. Inner loops aren't supported in the " |
| 7618 | "VPlan-native path.\n" ); |
| 7619 | return VectorizationFactor::Disabled(); |
| 7620 | } |
| 7621 | |
| 7622 | Optional<VectorizationFactor> |
| 7623 | LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) { |
| 7624 | assert(OrigLoop->isInnermost() && "Inner loop expected." ); |
| 7625 | Optional<ElementCount> MaybeMaxVF = CM.computeMaxVF(UserVF, UserIC); |
| 7626 | if (!MaybeMaxVF) // Cases that should not to be vectorized nor interleaved. |
| 7627 | return None; |
| 7628 | |
| 7629 | // Invalidate interleave groups if all blocks of loop will be predicated. |
| 7630 | if (CM.blockNeedsPredication(OrigLoop->getHeader()) && |
| 7631 | !useMaskedInterleavedAccesses(*TTI)) { |
| 7632 | LLVM_DEBUG( |
| 7633 | dbgs() |
| 7634 | << "LV: Invalidate all interleaved groups due to fold-tail by masking " |
| 7635 | "which requires masked-interleaved support.\n" ); |
| 7636 | if (CM.InterleaveInfo.invalidateGroups()) |
| 7637 | // Invalidating interleave groups also requires invalidating all decisions |
| 7638 | // based on them, which includes widening decisions and uniform and scalar |
| 7639 | // values. |
| 7640 | CM.invalidateCostModelingDecisions(); |
| 7641 | } |
| 7642 | |
| 7643 | ElementCount MaxVF = MaybeMaxVF.getValue(); |
| 7644 | assert(MaxVF.isNonZero() && "MaxVF is zero." ); |
| 7645 | |
| 7646 | bool UserVFIsLegal = ElementCount::isKnownLE(UserVF, MaxVF); |
| 7647 | if (!UserVF.isZero() && |
| 7648 | (UserVFIsLegal || (UserVF.isScalable() && MaxVF.isScalable()))) { |
| 7649 | // FIXME: MaxVF is temporarily used inplace of UserVF for illegal scalable |
| 7650 | // VFs here, this should be reverted to only use legal UserVFs once the |
| 7651 | // loop below supports scalable VFs. |
| 7652 | ElementCount VF = UserVFIsLegal ? UserVF : MaxVF; |
| 7653 | LLVM_DEBUG(dbgs() << "LV: Using " << (UserVFIsLegal ? "user" : "max" ) |
| 7654 | << " VF " << VF << ".\n" ); |
| 7655 | assert(isPowerOf2_32(VF.getKnownMinValue()) && |
| 7656 | "VF needs to be a power of two" ); |
| 7657 | // Collect the instructions (and their associated costs) that will be more |
| 7658 | // profitable to scalarize. |
| 7659 | CM.selectUserVectorizationFactor(VF); |
| 7660 | CM.collectInLoopReductions(); |
| 7661 | buildVPlansWithVPRecipes(VF, VF); |
| 7662 | LLVM_DEBUG(printPlans(dbgs())); |
| 7663 | return {{VF, 0}}; |
| 7664 | } |
| 7665 | |
| 7666 | assert(!MaxVF.isScalable() && |
| 7667 | "Scalable vectors not yet supported beyond this point" ); |
| 7668 | |
| 7669 | for (ElementCount VF = ElementCount::getFixed(1); |
| 7670 | ElementCount::isKnownLE(VF, MaxVF); VF *= 2) { |
| 7671 | // Collect Uniform and Scalar instructions after vectorization with VF. |
| 7672 | CM.collectUniformsAndScalars(VF); |
| 7673 | |
| 7674 | // Collect the instructions (and their associated costs) that will be more |
| 7675 | // profitable to scalarize. |
| 7676 | if (VF.isVector()) |
| 7677 | CM.collectInstsToScalarize(VF); |
| 7678 | } |
| 7679 | |
| 7680 | CM.collectInLoopReductions(); |
| 7681 | |
| 7682 | buildVPlansWithVPRecipes(ElementCount::getFixed(1), MaxVF); |
| 7683 | LLVM_DEBUG(printPlans(dbgs())); |
| 7684 | if (MaxVF.isScalar()) |
| 7685 | return VectorizationFactor::Disabled(); |
| 7686 | |
| 7687 | // Select the optimal vectorization factor. |
| 7688 | return CM.selectVectorizationFactor(MaxVF); |
| 7689 | } |
| 7690 | |
| 7691 | void LoopVectorizationPlanner::setBestPlan(ElementCount VF, unsigned UF) { |
| 7692 | LLVM_DEBUG(dbgs() << "Setting best plan to VF=" << VF << ", UF=" << UF |
| 7693 | << '\n'); |
| 7694 | BestVF = VF; |
| 7695 | BestUF = UF; |
| 7696 | |
| 7697 | erase_if(VPlans, [VF](const VPlanPtr &Plan) { |
| 7698 | return !Plan->hasVF(VF); |
| 7699 | }); |
| 7700 | assert(VPlans.size() == 1 && "Best VF has not a single VPlan." ); |
| 7701 | } |
| 7702 | |
| 7703 | void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV, |
| 7704 | DominatorTree *DT) { |
| 7705 | // Perform the actual loop transformation. |
| 7706 | |
| 7707 | // 1. Create a new empty loop. Unlink the old loop and connect the new one. |
| 7708 | VPCallbackILV CallbackILV(ILV); |
| 7709 | |
| 7710 | assert(BestVF.hasValue() && "Vectorization Factor is missing" ); |
| 7711 | |
| 7712 | VPTransformState State{*BestVF, |
| 7713 | BestUF, |
| 7714 | OrigLoop, |
| 7715 | LI, |
| 7716 | DT, |
| 7717 | ILV.Builder, |
| 7718 | ILV.VectorLoopValueMap, |
| 7719 | &ILV, |
| 7720 | CallbackILV}; |
| 7721 | State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton(); |
| 7722 | State.TripCount = ILV.getOrCreateTripCount(nullptr); |
| 7723 | State.CanonicalIV = ILV.Induction; |
| 7724 | |
| 7725 | ILV.printDebugTracesAtStart(); |
| 7726 | |
| 7727 | //===------------------------------------------------===// |
| 7728 | // |
| 7729 | // Notice: any optimization or new instruction that go |
| 7730 | // into the code below should also be implemented in |
| 7731 | // the cost-model. |
| 7732 | // |
| 7733 | //===------------------------------------------------===// |
| 7734 | |
| 7735 | // 2. Copy and widen instructions from the old loop into the new loop. |
| 7736 | assert(VPlans.size() == 1 && "Not a single VPlan to execute." ); |
| 7737 | VPlans.front()->execute(&State); |
| 7738 | |
| 7739 | // 3. Fix the vectorized code: take care of header phi's, live-outs, |
| 7740 | // predication, updating analyses. |
| 7741 | ILV.fixVectorizedLoop(); |
| 7742 | |
| 7743 | ILV.printDebugTracesAtEnd(); |
| 7744 | } |
| 7745 | |
| 7746 | void LoopVectorizationPlanner::collectTriviallyDeadInstructions( |
| 7747 | SmallPtrSetImpl<Instruction *> &DeadInstructions) { |
| 7748 | |
| 7749 | // We create new control-flow for the vectorized loop, so the original exit |
| 7750 | // conditions will be dead after vectorization if it's only used by the |
| 7751 | // terminator |
| 7752 | SmallVector<BasicBlock*> ExitingBlocks; |
| 7753 | OrigLoop->getExitingBlocks(ExitingBlocks); |
| 7754 | for (auto *BB : ExitingBlocks) { |
| 7755 | auto *Cmp = dyn_cast<Instruction>(BB->getTerminator()->getOperand(0)); |
| 7756 | if (!Cmp || !Cmp->hasOneUse()) |
| 7757 | continue; |
| 7758 | |
| 7759 | // TODO: we should introduce a getUniqueExitingBlocks on Loop |
| 7760 | if (!DeadInstructions.insert(Cmp).second) |
| 7761 | continue; |
| 7762 | |
| 7763 | // The operands of the icmp is often a dead trunc, used by IndUpdate. |
| 7764 | // TODO: can recurse through operands in general |
| 7765 | for (Value *Op : Cmp->operands()) { |
| 7766 | if (isa<TruncInst>(Op) && Op->hasOneUse()) |
| 7767 | DeadInstructions.insert(cast<Instruction>(Op)); |
| 7768 | } |
| 7769 | } |
| 7770 | |
| 7771 | // We create new "steps" for induction variable updates to which the original |
| 7772 | // induction variables map. An original update instruction will be dead if |
| 7773 | // all its users except the induction variable are dead. |
| 7774 | auto *Latch = OrigLoop->getLoopLatch(); |
| 7775 | for (auto &Induction : Legal->getInductionVars()) { |
| 7776 | PHINode *Ind = Induction.first; |
| 7777 | auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| 7778 | |
| 7779 | // If the tail is to be folded by masking, the primary induction variable, |
| 7780 | // if exists, isn't dead: it will be used for masking. Don't kill it. |
| 7781 | if (CM.foldTailByMasking() && IndUpdate == Legal->getPrimaryInduction()) |
| 7782 | continue; |
| 7783 | |
| 7784 | if (llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| 7785 | return U == Ind || DeadInstructions.count(cast<Instruction>(U)); |
| 7786 | })) |
| 7787 | DeadInstructions.insert(IndUpdate); |
| 7788 | |
| 7789 | // We record as "Dead" also the type-casting instructions we had identified |
| 7790 | // during induction analysis. We don't need any handling for them in the |
| 7791 | // vectorized loop because we have proven that, under a proper runtime |
| 7792 | // test guarding the vectorized loop, the value of the phi, and the casted |
| 7793 | // value of the phi, are the same. The last instruction in this casting chain |
| 7794 | // will get its scalar/vector/widened def from the scalar/vector/widened def |
| 7795 | // of the respective phi node. Any other casts in the induction def-use chain |
| 7796 | // have no other uses outside the phi update chain, and will be ignored. |
| 7797 | InductionDescriptor &IndDes = Induction.second; |
| 7798 | const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts(); |
| 7799 | DeadInstructions.insert(Casts.begin(), Casts.end()); |
| 7800 | } |
| 7801 | } |
| 7802 | |
| 7803 | Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; } |
| 7804 | |
| 7805 | Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; } |
| 7806 | |
| 7807 | Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step, |
| 7808 | Instruction::BinaryOps BinOp) { |
| 7809 | // When unrolling and the VF is 1, we only need to add a simple scalar. |
| 7810 | Type *Ty = Val->getType(); |
| 7811 | assert(!Ty->isVectorTy() && "Val must be a scalar" ); |
| 7812 | |
| 7813 | if (Ty->isFloatingPointTy()) { |
| 7814 | Constant *C = ConstantFP::get(Ty, (double)StartIdx); |
| 7815 | |
| 7816 | // Floating point operations had to be 'fast' to enable the unrolling. |
| 7817 | Value *MulOp = addFastMathFlag(Builder.CreateFMul(C, Step)); |
| 7818 | return addFastMathFlag(Builder.CreateBinOp(BinOp, Val, MulOp)); |
| 7819 | } |
| 7820 | Constant *C = ConstantInt::get(Ty, StartIdx); |
| 7821 | return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction" ); |
| 7822 | } |
| 7823 | |
| 7824 | static void AddRuntimeUnrollDisableMetaData(Loop *L) { |
| 7825 | SmallVector<Metadata *, 4> MDs; |
| 7826 | // Reserve first location for self reference to the LoopID metadata node. |
| 7827 | MDs.push_back(nullptr); |
| 7828 | bool IsUnrollMetadata = false; |
| 7829 | MDNode *LoopID = L->getLoopID(); |
| 7830 | if (LoopID) { |
| 7831 | // First find existing loop unrolling disable metadata. |
| 7832 | for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) { |
| 7833 | auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i)); |
| 7834 | if (MD) { |
| 7835 | const auto *S = dyn_cast<MDString>(MD->getOperand(0)); |
| 7836 | IsUnrollMetadata = |
| 7837 | S && S->getString().startswith("llvm.loop.unroll.disable" ); |
| 7838 | } |
| 7839 | MDs.push_back(LoopID->getOperand(i)); |
| 7840 | } |
| 7841 | } |
| 7842 | |
| 7843 | if (!IsUnrollMetadata) { |
| 7844 | // Add runtime unroll disable metadata. |
| 7845 | LLVMContext &Context = L->getHeader()->getContext(); |
| 7846 | SmallVector<Metadata *, 1> DisableOperands; |
| 7847 | DisableOperands.push_back( |
| 7848 | MDString::get(Context, "llvm.loop.unroll.runtime.disable" )); |
| 7849 | MDNode *DisableNode = MDNode::get(Context, DisableOperands); |
| 7850 | MDs.push_back(DisableNode); |
| 7851 | MDNode *NewLoopID = MDNode::get(Context, MDs); |
| 7852 | // Set operand 0 to refer to the loop id itself. |
| 7853 | NewLoopID->replaceOperandWith(0, NewLoopID); |
| 7854 | L->setLoopID(NewLoopID); |
| 7855 | } |
| 7856 | } |
| 7857 | |
| 7858 | //===--------------------------------------------------------------------===// |
| 7859 | // EpilogueVectorizerMainLoop |
| 7860 | //===--------------------------------------------------------------------===// |
| 7861 | |
| 7862 | /// This function is partially responsible for generating the control flow |
| 7863 | /// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization. |
| 7864 | BasicBlock *EpilogueVectorizerMainLoop::createEpilogueVectorizedLoopSkeleton() { |
| 7865 | MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| 7866 | Loop *Lp = createVectorLoopSkeleton("" ); |
| 7867 | |
| 7868 | // Generate the code to check the minimum iteration count of the vector |
| 7869 | // epilogue (see below). |
| 7870 | EPI.EpilogueIterationCountCheck = |
| 7871 | emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader, true); |
| 7872 | EPI.EpilogueIterationCountCheck->setName("iter.check" ); |
| 7873 | |
| 7874 | // Generate the code to check any assumptions that we've made for SCEV |
| 7875 | // expressions. |
| 7876 | BasicBlock * = LoopVectorPreHeader; |
| 7877 | emitSCEVChecks(Lp, LoopScalarPreHeader); |
| 7878 | |
| 7879 | // If a safety check was generated save it. |
| 7880 | if (SavedPreHeader != LoopVectorPreHeader) |
| 7881 | EPI.SCEVSafetyCheck = SavedPreHeader; |
| 7882 | |
| 7883 | // Generate the code that checks at runtime if arrays overlap. We put the |
| 7884 | // checks into a separate block to make the more common case of few elements |
| 7885 | // faster. |
| 7886 | SavedPreHeader = LoopVectorPreHeader; |
| 7887 | emitMemRuntimeChecks(Lp, LoopScalarPreHeader); |
| 7888 | |
| 7889 | // If a safety check was generated save/overwite it. |
| 7890 | if (SavedPreHeader != LoopVectorPreHeader) |
| 7891 | EPI.MemSafetyCheck = SavedPreHeader; |
| 7892 | |
| 7893 | // Generate the iteration count check for the main loop, *after* the check |
| 7894 | // for the epilogue loop, so that the path-length is shorter for the case |
| 7895 | // that goes directly through the vector epilogue. The longer-path length for |
| 7896 | // the main loop is compensated for, by the gain from vectorizing the larger |
| 7897 | // trip count. Note: the branch will get updated later on when we vectorize |
| 7898 | // the epilogue. |
| 7899 | EPI.MainLoopIterationCountCheck = |
| 7900 | emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader, false); |
| 7901 | |
| 7902 | // Generate the induction variable. |
| 7903 | OldInduction = Legal->getPrimaryInduction(); |
| 7904 | Type *IdxTy = Legal->getWidestInductionType(); |
| 7905 | Value *StartIdx = ConstantInt::get(IdxTy, 0); |
| 7906 | Constant *Step = ConstantInt::get(IdxTy, VF.getKnownMinValue() * UF); |
| 7907 | Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| 7908 | EPI.VectorTripCount = CountRoundDown; |
| 7909 | Induction = |
| 7910 | createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| 7911 | getDebugLocFromInstOrOperands(OldInduction)); |
| 7912 | |
| 7913 | // Skip induction resume value creation here because they will be created in |
| 7914 | // the second pass. If we created them here, they wouldn't be used anyway, |
| 7915 | // because the vplan in the second pass still contains the inductions from the |
| 7916 | // original loop. |
| 7917 | |
| 7918 | return completeLoopSkeleton(Lp, OrigLoopID); |
| 7919 | } |
| 7920 | |
| 7921 | void EpilogueVectorizerMainLoop::printDebugTracesAtStart() { |
| 7922 | LLVM_DEBUG({ |
| 7923 | dbgs() << "Create Skeleton for epilogue vectorized loop (first pass)\n" |
| 7924 | << "Main Loop VF:" << EPI.MainLoopVF.getKnownMinValue() |
| 7925 | << ", Main Loop UF:" << EPI.MainLoopUF |
| 7926 | << ", Epilogue Loop VF:" << EPI.EpilogueVF.getKnownMinValue() |
| 7927 | << ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n" ; |
| 7928 | }); |
| 7929 | } |
| 7930 | |
| 7931 | void EpilogueVectorizerMainLoop::printDebugTracesAtEnd() { |
| 7932 | DEBUG_WITH_TYPE(VerboseDebug, { |
| 7933 | dbgs() << "intermediate fn:\n" << *Induction->getFunction() << "\n" ; |
| 7934 | }); |
| 7935 | } |
| 7936 | |
| 7937 | BasicBlock *EpilogueVectorizerMainLoop::emitMinimumIterationCountCheck( |
| 7938 | Loop *L, BasicBlock *Bypass, bool ForEpilogue) { |
| 7939 | assert(L && "Expected valid Loop." ); |
| 7940 | assert(Bypass && "Expected valid bypass basic block." ); |
| 7941 | unsigned VFactor = |
| 7942 | ForEpilogue ? EPI.EpilogueVF.getKnownMinValue() : VF.getKnownMinValue(); |
| 7943 | unsigned UFactor = ForEpilogue ? EPI.EpilogueUF : UF; |
| 7944 | Value *Count = getOrCreateTripCount(L); |
| 7945 | // Reuse existing vector loop preheader for TC checks. |
| 7946 | // Note that new preheader block is generated for vector loop. |
| 7947 | BasicBlock *const TCCheckBlock = LoopVectorPreHeader; |
| 7948 | IRBuilder<> Builder(TCCheckBlock->getTerminator()); |
| 7949 | |
| 7950 | // Generate code to check if the loop's trip count is less than VF * UF of the |
| 7951 | // main vector loop. |
| 7952 | auto P = |
| 7953 | Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; |
| 7954 | |
| 7955 | Value *CheckMinIters = Builder.CreateICmp( |
| 7956 | P, Count, ConstantInt::get(Count->getType(), VFactor * UFactor), |
| 7957 | "min.iters.check" ); |
| 7958 | |
| 7959 | if (!ForEpilogue) |
| 7960 | TCCheckBlock->setName("vector.main.loop.iter.check" ); |
| 7961 | |
| 7962 | // Create new preheader for vector loop. |
| 7963 | LoopVectorPreHeader = SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), |
| 7964 | DT, LI, nullptr, "vector.ph" ); |
| 7965 | |
| 7966 | if (ForEpilogue) { |
| 7967 | assert(DT->properlyDominates(DT->getNode(TCCheckBlock), |
| 7968 | DT->getNode(Bypass)->getIDom()) && |
| 7969 | "TC check is expected to dominate Bypass" ); |
| 7970 | |
| 7971 | // Update dominator for Bypass & LoopExit. |
| 7972 | DT->changeImmediateDominator(Bypass, TCCheckBlock); |
| 7973 | DT->changeImmediateDominator(LoopExitBlock, TCCheckBlock); |
| 7974 | |
| 7975 | LoopBypassBlocks.push_back(TCCheckBlock); |
| 7976 | |
| 7977 | // Save the trip count so we don't have to regenerate it in the |
| 7978 | // vec.epilog.iter.check. This is safe to do because the trip count |
| 7979 | // generated here dominates the vector epilog iter check. |
| 7980 | EPI.TripCount = Count; |
| 7981 | } |
| 7982 | |
| 7983 | ReplaceInstWithInst( |
| 7984 | TCCheckBlock->getTerminator(), |
| 7985 | BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| 7986 | |
| 7987 | return TCCheckBlock; |
| 7988 | } |
| 7989 | |
| 7990 | //===--------------------------------------------------------------------===// |
| 7991 | // EpilogueVectorizerEpilogueLoop |
| 7992 | //===--------------------------------------------------------------------===// |
| 7993 | |
| 7994 | /// This function is partially responsible for generating the control flow |
| 7995 | /// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization. |
| 7996 | BasicBlock * |
| 7997 | EpilogueVectorizerEpilogueLoop::createEpilogueVectorizedLoopSkeleton() { |
| 7998 | MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| 7999 | Loop *Lp = createVectorLoopSkeleton("vec.epilog." ); |
| 8000 | |
| 8001 | // Now, compare the remaining count and if there aren't enough iterations to |
| 8002 | // execute the vectorized epilogue skip to the scalar part. |
| 8003 | BasicBlock *VecEpilogueIterationCountCheck = LoopVectorPreHeader; |
| 8004 | VecEpilogueIterationCountCheck->setName("vec.epilog.iter.check" ); |
| 8005 | LoopVectorPreHeader = |
| 8006 | SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| 8007 | LI, nullptr, "vec.epilog.ph" ); |
| 8008 | emitMinimumVectorEpilogueIterCountCheck(Lp, LoopScalarPreHeader, |
| 8009 | VecEpilogueIterationCountCheck); |
| 8010 | |
| 8011 | // Adjust the control flow taking the state info from the main loop |
| 8012 | // vectorization into account. |
| 8013 | assert(EPI.MainLoopIterationCountCheck && EPI.EpilogueIterationCountCheck && |
| 8014 | "expected this to be saved from the previous pass." ); |
| 8015 | EPI.MainLoopIterationCountCheck->getTerminator()->replaceUsesOfWith( |
| 8016 | VecEpilogueIterationCountCheck, LoopVectorPreHeader); |
| 8017 | |
| 8018 | DT->changeImmediateDominator(LoopVectorPreHeader, |
| 8019 | EPI.MainLoopIterationCountCheck); |
| 8020 | |
| 8021 | EPI.EpilogueIterationCountCheck->getTerminator()->replaceUsesOfWith( |
| 8022 | VecEpilogueIterationCountCheck, LoopScalarPreHeader); |
| 8023 | |
| 8024 | if (EPI.SCEVSafetyCheck) |
| 8025 | EPI.SCEVSafetyCheck->getTerminator()->replaceUsesOfWith( |
| 8026 | VecEpilogueIterationCountCheck, LoopScalarPreHeader); |
| 8027 | if (EPI.MemSafetyCheck) |
| 8028 | EPI.MemSafetyCheck->getTerminator()->replaceUsesOfWith( |
| 8029 | VecEpilogueIterationCountCheck, LoopScalarPreHeader); |
| 8030 | |
| 8031 | DT->changeImmediateDominator( |
| 8032 | VecEpilogueIterationCountCheck, |
| 8033 | VecEpilogueIterationCountCheck->getSinglePredecessor()); |
| 8034 | |
| 8035 | DT->changeImmediateDominator(LoopScalarPreHeader, |
| 8036 | EPI.EpilogueIterationCountCheck); |
| 8037 | DT->changeImmediateDominator(LoopExitBlock, EPI.EpilogueIterationCountCheck); |
| 8038 | |
| 8039 | // Keep track of bypass blocks, as they feed start values to the induction |
| 8040 | // phis in the scalar loop preheader. |
| 8041 | if (EPI.SCEVSafetyCheck) |
| 8042 | LoopBypassBlocks.push_back(EPI.SCEVSafetyCheck); |
| 8043 | if (EPI.MemSafetyCheck) |
| 8044 | LoopBypassBlocks.push_back(EPI.MemSafetyCheck); |
| 8045 | LoopBypassBlocks.push_back(EPI.EpilogueIterationCountCheck); |
| 8046 | |
| 8047 | // Generate a resume induction for the vector epilogue and put it in the |
| 8048 | // vector epilogue preheader |
| 8049 | Type *IdxTy = Legal->getWidestInductionType(); |
| 8050 | PHINode *EPResumeVal = PHINode::Create(IdxTy, 2, "vec.epilog.resume.val" , |
| 8051 | LoopVectorPreHeader->getFirstNonPHI()); |
| 8052 | EPResumeVal->addIncoming(EPI.VectorTripCount, VecEpilogueIterationCountCheck); |
| 8053 | EPResumeVal->addIncoming(ConstantInt::get(IdxTy, 0), |
| 8054 | EPI.MainLoopIterationCountCheck); |
| 8055 | |
| 8056 | // Generate the induction variable. |
| 8057 | OldInduction = Legal->getPrimaryInduction(); |
| 8058 | Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| 8059 | Constant *Step = ConstantInt::get(IdxTy, VF.getKnownMinValue() * UF); |
| 8060 | Value *StartIdx = EPResumeVal; |
| 8061 | Induction = |
| 8062 | createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| 8063 | getDebugLocFromInstOrOperands(OldInduction)); |
| 8064 | |
| 8065 | // Generate induction resume values. These variables save the new starting |
| 8066 | // indexes for the scalar loop. They are used to test if there are any tail |
| 8067 | // iterations left once the vector loop has completed. |
| 8068 | // Note that when the vectorized epilogue is skipped due to iteration count |
| 8069 | // check, then the resume value for the induction variable comes from |
| 8070 | // the trip count of the main vector loop, hence passing the AdditionalBypass |
| 8071 | // argument. |
| 8072 | createInductionResumeValues(Lp, CountRoundDown, |
| 8073 | {VecEpilogueIterationCountCheck, |
| 8074 | EPI.VectorTripCount} /* AdditionalBypass */); |
| 8075 | |
| 8076 | AddRuntimeUnrollDisableMetaData(Lp); |
| 8077 | return completeLoopSkeleton(Lp, OrigLoopID); |
| 8078 | } |
| 8079 | |
| 8080 | BasicBlock * |
| 8081 | EpilogueVectorizerEpilogueLoop::emitMinimumVectorEpilogueIterCountCheck( |
| 8082 | Loop *L, BasicBlock *Bypass, BasicBlock *Insert) { |
| 8083 | |
| 8084 | assert(EPI.TripCount && |
| 8085 | "Expected trip count to have been safed in the first pass." ); |
| 8086 | assert( |
| 8087 | (!isa<Instruction>(EPI.TripCount) || |
| 8088 | DT->dominates(cast<Instruction>(EPI.TripCount)->getParent(), Insert)) && |
| 8089 | "saved trip count does not dominate insertion point." ); |
| 8090 | Value *TC = EPI.TripCount; |
| 8091 | IRBuilder<> Builder(Insert->getTerminator()); |
| 8092 | Value *Count = Builder.CreateSub(TC, EPI.VectorTripCount, "n.vec.remaining" ); |
| 8093 | |
| 8094 | // Generate code to check if the loop's trip count is less than VF * UF of the |
| 8095 | // vector epilogue loop. |
| 8096 | auto P = |
| 8097 | Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; |
| 8098 | |
| 8099 | Value *CheckMinIters = Builder.CreateICmp( |
| 8100 | P, Count, |
| 8101 | ConstantInt::get(Count->getType(), |
| 8102 | EPI.EpilogueVF.getKnownMinValue() * EPI.EpilogueUF), |
| 8103 | "min.epilog.iters.check" ); |
| 8104 | |
| 8105 | ReplaceInstWithInst( |
| 8106 | Insert->getTerminator(), |
| 8107 | BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| 8108 | |
| 8109 | LoopBypassBlocks.push_back(Insert); |
| 8110 | return Insert; |
| 8111 | } |
| 8112 | |
| 8113 | void EpilogueVectorizerEpilogueLoop::printDebugTracesAtStart() { |
| 8114 | LLVM_DEBUG({ |
| 8115 | dbgs() << "Create Skeleton for epilogue vectorized loop (second pass)\n" |
| 8116 | << "Main Loop VF:" << EPI.MainLoopVF.getKnownMinValue() |
| 8117 | << ", Main Loop UF:" << EPI.MainLoopUF |
| 8118 | << ", Epilogue Loop VF:" << EPI.EpilogueVF.getKnownMinValue() |
| 8119 | << ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n" ; |
| 8120 | }); |
| 8121 | } |
| 8122 | |
| 8123 | void EpilogueVectorizerEpilogueLoop::printDebugTracesAtEnd() { |
| 8124 | DEBUG_WITH_TYPE(VerboseDebug, { |
| 8125 | dbgs() << "final fn:\n" << *Induction->getFunction() << "\n" ; |
| 8126 | }); |
| 8127 | } |
| 8128 | |
| 8129 | bool LoopVectorizationPlanner::getDecisionAndClampRange( |
| 8130 | const std::function<bool(ElementCount)> &Predicate, VFRange &Range) { |
| 8131 | assert(!Range.isEmpty() && "Trying to test an empty VF range." ); |
| 8132 | bool PredicateAtRangeStart = Predicate(Range.Start); |
| 8133 | |
| 8134 | for (ElementCount TmpVF = Range.Start * 2; |
| 8135 | ElementCount::isKnownLT(TmpVF, Range.End); TmpVF *= 2) |
| 8136 | if (Predicate(TmpVF) != PredicateAtRangeStart) { |
| 8137 | Range.End = TmpVF; |
| 8138 | break; |
| 8139 | } |
| 8140 | |
| 8141 | return PredicateAtRangeStart; |
| 8142 | } |
| 8143 | |
| 8144 | /// Build VPlans for the full range of feasible VF's = {\p MinVF, 2 * \p MinVF, |
| 8145 | /// 4 * \p MinVF, ..., \p MaxVF} by repeatedly building a VPlan for a sub-range |
| 8146 | /// of VF's starting at a given VF and extending it as much as possible. Each |
| 8147 | /// vectorization decision can potentially shorten this sub-range during |
| 8148 | /// buildVPlan(). |
| 8149 | void LoopVectorizationPlanner::buildVPlans(ElementCount MinVF, |
| 8150 | ElementCount MaxVF) { |
| 8151 | auto MaxVFPlusOne = MaxVF.getWithIncrement(1); |
| 8152 | for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFPlusOne);) { |
| 8153 | VFRange SubRange = {VF, MaxVFPlusOne}; |
| 8154 | VPlans.push_back(buildVPlan(SubRange)); |
| 8155 | VF = SubRange.End; |
| 8156 | } |
| 8157 | } |
| 8158 | |
| 8159 | VPValue *VPRecipeBuilder::createEdgeMask(BasicBlock *Src, BasicBlock *Dst, |
| 8160 | VPlanPtr &Plan) { |
| 8161 | assert(is_contained(predecessors(Dst), Src) && "Invalid edge" ); |
| 8162 | |
| 8163 | // Look for cached value. |
| 8164 | std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst); |
| 8165 | EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge); |
| 8166 | if (ECEntryIt != EdgeMaskCache.end()) |
| 8167 | return ECEntryIt->second; |
| 8168 | |
| 8169 | VPValue *SrcMask = createBlockInMask(Src, Plan); |
| 8170 | |
| 8171 | // The terminator has to be a branch inst! |
| 8172 | BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator()); |
| 8173 | assert(BI && "Unexpected terminator found" ); |
| 8174 | |
| 8175 | if (!BI->isConditional() || BI->getSuccessor(0) == BI->getSuccessor(1)) |
| 8176 | return EdgeMaskCache[Edge] = SrcMask; |
| 8177 | |
| 8178 | // If source is an exiting block, we know the exit edge is dynamically dead |
| 8179 | // in the vector loop, and thus we don't need to restrict the mask. Avoid |
| 8180 | // adding uses of an otherwise potentially dead instruction. |
| 8181 | if (OrigLoop->isLoopExiting(Src)) |
| 8182 | return EdgeMaskCache[Edge] = SrcMask; |
| 8183 | |
| 8184 | VPValue *EdgeMask = Plan->getOrAddVPValue(BI->getCondition()); |
| 8185 | assert(EdgeMask && "No Edge Mask found for condition" ); |
| 8186 | |
| 8187 | if (BI->getSuccessor(0) != Dst) |
| 8188 | EdgeMask = Builder.createNot(EdgeMask); |
| 8189 | |
| 8190 | if (SrcMask) { // Otherwise block in-mask is all-one, no need to AND. |
| 8191 | // The condition is 'SrcMask && EdgeMask', which is equivalent to |
| 8192 | // 'select i1 SrcMask, i1 EdgeMask, i1 false'. |
| 8193 | // The select version does not introduce new UB if SrcMask is false and |
| 8194 | // EdgeMask is poison. Using 'and' here introduces undefined behavior. |
| 8195 | VPValue *False = Plan->getOrAddVPValue( |
| 8196 | ConstantInt::getFalse(BI->getCondition()->getType())); |
| 8197 | EdgeMask = Builder.createSelect(SrcMask, EdgeMask, False); |
| 8198 | } |
| 8199 | |
| 8200 | return EdgeMaskCache[Edge] = EdgeMask; |
| 8201 | } |
| 8202 | |
| 8203 | VPValue *VPRecipeBuilder::createBlockInMask(BasicBlock *BB, VPlanPtr &Plan) { |
| 8204 | assert(OrigLoop->contains(BB) && "Block is not a part of a loop" ); |
| 8205 | |
| 8206 | // Look for cached value. |
| 8207 | BlockMaskCacheTy::iterator BCEntryIt = BlockMaskCache.find(BB); |
| 8208 | if (BCEntryIt != BlockMaskCache.end()) |
| 8209 | return BCEntryIt->second; |
| 8210 | |
| 8211 | // All-one mask is modelled as no-mask following the convention for masked |
| 8212 | // load/store/gather/scatter. Initialize BlockMask to no-mask. |
| 8213 | VPValue *BlockMask = nullptr; |
| 8214 | |
| 8215 | if (OrigLoop->getHeader() == BB) { |
| 8216 | if (!CM.blockNeedsPredication(BB)) |
| 8217 | return BlockMaskCache[BB] = BlockMask; // Loop incoming mask is all-one. |
| 8218 | |
| 8219 | // Create the block in mask as the first non-phi instruction in the block. |
| 8220 | VPBuilder::InsertPointGuard Guard(Builder); |
| 8221 | auto NewInsertionPoint = Builder.getInsertBlock()->getFirstNonPhi(); |
| 8222 | Builder.setInsertPoint(Builder.getInsertBlock(), NewInsertionPoint); |
| 8223 | |
| 8224 | // Introduce the early-exit compare IV <= BTC to form header block mask. |
| 8225 | // This is used instead of IV < TC because TC may wrap, unlike BTC. |
| 8226 | // Start by constructing the desired canonical IV. |
| 8227 | VPValue *IV = nullptr; |
| 8228 | if (Legal->getPrimaryInduction()) |
| 8229 | IV = Plan->getOrAddVPValue(Legal->getPrimaryInduction()); |
| 8230 | else { |
| 8231 | auto IVRecipe = new VPWidenCanonicalIVRecipe(); |
| 8232 | Builder.getInsertBlock()->insert(IVRecipe, NewInsertionPoint); |
| 8233 | IV = IVRecipe->getVPValue(); |
| 8234 | } |
| 8235 | VPValue *BTC = Plan->getOrCreateBackedgeTakenCount(); |
| 8236 | bool TailFolded = !CM.isScalarEpilogueAllowed(); |
| 8237 | |
| 8238 | if (TailFolded && CM.TTI.emitGetActiveLaneMask()) { |
| 8239 | // While ActiveLaneMask is a binary op that consumes the loop tripcount |
| 8240 | // as a second argument, we only pass the IV here and extract the |
| 8241 | // tripcount from the transform state where codegen of the VP instructions |
| 8242 | // happen. |
| 8243 | BlockMask = Builder.createNaryOp(VPInstruction::ActiveLaneMask, {IV}); |
| 8244 | } else { |
| 8245 | BlockMask = Builder.createNaryOp(VPInstruction::ICmpULE, {IV, BTC}); |
| 8246 | } |
| 8247 | return BlockMaskCache[BB] = BlockMask; |
| 8248 | } |
| 8249 | |
| 8250 | // This is the block mask. We OR all incoming edges. |
| 8251 | for (auto *Predecessor : predecessors(BB)) { |
| 8252 | VPValue *EdgeMask = createEdgeMask(Predecessor, BB, Plan); |
| 8253 | if (!EdgeMask) // Mask of predecessor is all-one so mask of block is too. |
| 8254 | return BlockMaskCache[BB] = EdgeMask; |
| 8255 | |
| 8256 | if (!BlockMask) { // BlockMask has its initialized nullptr value. |
| 8257 | BlockMask = EdgeMask; |
| 8258 | continue; |
| 8259 | } |
| 8260 | |
| 8261 | BlockMask = Builder.createOr(BlockMask, EdgeMask); |
| 8262 | } |
| 8263 | |
| 8264 | return BlockMaskCache[BB] = BlockMask; |
| 8265 | } |
| 8266 | |
| 8267 | VPRecipeBase *VPRecipeBuilder::tryToWidenMemory(Instruction *I, VFRange &Range, |
| 8268 | VPlanPtr &Plan) { |
| 8269 | assert((isa<LoadInst>(I) || isa<StoreInst>(I)) && |
| 8270 | "Must be called with either a load or store" ); |
| 8271 | |
| 8272 | auto willWiden = [&](ElementCount VF) -> bool { |
| 8273 | if (VF.isScalar()) |
| 8274 | return false; |
| 8275 | LoopVectorizationCostModel::InstWidening Decision = |
| 8276 | CM.getWideningDecision(I, VF); |
| 8277 | assert(Decision != LoopVectorizationCostModel::CM_Unknown && |
| 8278 | "CM decision should be taken at this point." ); |
| 8279 | if (Decision == LoopVectorizationCostModel::CM_Interleave) |
| 8280 | return true; |
| 8281 | if (CM.isScalarAfterVectorization(I, VF) || |
| 8282 | CM.isProfitableToScalarize(I, VF)) |
| 8283 | return false; |
| 8284 | return Decision != LoopVectorizationCostModel::CM_Scalarize; |
| 8285 | }; |
| 8286 | |
| 8287 | if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range)) |
| 8288 | return nullptr; |
| 8289 | |
| 8290 | VPValue *Mask = nullptr; |
| 8291 | if (Legal->isMaskRequired(I)) |
| 8292 | Mask = createBlockInMask(I->getParent(), Plan); |
| 8293 | |
| 8294 | VPValue *Addr = Plan->getOrAddVPValue(getLoadStorePointerOperand(I)); |
| 8295 | if (LoadInst *Load = dyn_cast<LoadInst>(I)) |
| 8296 | return new VPWidenMemoryInstructionRecipe(*Load, Addr, Mask); |
| 8297 | |
| 8298 | StoreInst *Store = cast<StoreInst>(I); |
| 8299 | VPValue *StoredValue = Plan->getOrAddVPValue(Store->getValueOperand()); |
| 8300 | return new VPWidenMemoryInstructionRecipe(*Store, Addr, StoredValue, Mask); |
| 8301 | } |
| 8302 | |
| 8303 | VPWidenIntOrFpInductionRecipe * |
| 8304 | VPRecipeBuilder::tryToOptimizeInductionPHI(PHINode *Phi, VPlan &Plan) const { |
| 8305 | // Check if this is an integer or fp induction. If so, build the recipe that |
| 8306 | // produces its scalar and vector values. |
| 8307 | InductionDescriptor II = Legal->getInductionVars().lookup(Phi); |
| 8308 | if (II.getKind() == InductionDescriptor::IK_IntInduction || |
| 8309 | II.getKind() == InductionDescriptor::IK_FpInduction) { |
| 8310 | VPValue *Start = Plan.getOrAddVPValue(II.getStartValue()); |
| 8311 | return new VPWidenIntOrFpInductionRecipe(Phi, Start); |
| 8312 | } |
| 8313 | |
| 8314 | return nullptr; |
| 8315 | } |
| 8316 | |
| 8317 | VPWidenIntOrFpInductionRecipe * |
| 8318 | VPRecipeBuilder::tryToOptimizeInductionTruncate(TruncInst *I, VFRange &Range, |
| 8319 | VPlan &Plan) const { |
| 8320 | // Optimize the special case where the source is a constant integer |
| 8321 | // induction variable. Notice that we can only optimize the 'trunc' case |
| 8322 | // because (a) FP conversions lose precision, (b) sext/zext may wrap, and |
| 8323 | // (c) other casts depend on pointer size. |
| 8324 | |
| 8325 | // Determine whether \p K is a truncation based on an induction variable that |
| 8326 | // can be optimized. |
| 8327 | auto isOptimizableIVTruncate = |
| 8328 | [&](Instruction *K) -> std::function<bool(ElementCount)> { |
| 8329 | return [=](ElementCount VF) -> bool { |
| 8330 | return CM.isOptimizableIVTruncate(K, VF); |
| 8331 | }; |
| 8332 | }; |
| 8333 | |
| 8334 | if (LoopVectorizationPlanner::getDecisionAndClampRange( |
| 8335 | isOptimizableIVTruncate(I), Range)) { |
| 8336 | |
| 8337 | InductionDescriptor II = |
| 8338 | Legal->getInductionVars().lookup(cast<PHINode>(I->getOperand(0))); |
| 8339 | VPValue *Start = Plan.getOrAddVPValue(II.getStartValue()); |
| 8340 | return new VPWidenIntOrFpInductionRecipe(cast<PHINode>(I->getOperand(0)), |
| 8341 | Start, I); |
| 8342 | } |
| 8343 | return nullptr; |
| 8344 | } |
| 8345 | |
| 8346 | VPBlendRecipe *VPRecipeBuilder::tryToBlend(PHINode *Phi, VPlanPtr &Plan) { |
| 8347 | // We know that all PHIs in non-header blocks are converted into selects, so |
| 8348 | // we don't have to worry about the insertion order and we can just use the |
| 8349 | // builder. At this point we generate the predication tree. There may be |
| 8350 | // duplications since this is a simple recursive scan, but future |
| 8351 | // optimizations will clean it up. |
| 8352 | |
| 8353 | SmallVector<VPValue *, 2> Operands; |
| 8354 | unsigned NumIncoming = Phi->getNumIncomingValues(); |
| 8355 | for (unsigned In = 0; In < NumIncoming; In++) { |
| 8356 | VPValue *EdgeMask = |
| 8357 | createEdgeMask(Phi->getIncomingBlock(In), Phi->getParent(), Plan); |
| 8358 | assert((EdgeMask || NumIncoming == 1) && |
| 8359 | "Multiple predecessors with one having a full mask" ); |
| 8360 | Operands.push_back(Plan->getOrAddVPValue(Phi->getIncomingValue(In))); |
| 8361 | if (EdgeMask) |
| 8362 | Operands.push_back(EdgeMask); |
| 8363 | } |
| 8364 | return new VPBlendRecipe(Phi, Operands); |
| 8365 | } |
| 8366 | |
| 8367 | VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI, VFRange &Range, |
| 8368 | VPlan &Plan) const { |
| 8369 | |
| 8370 | bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( |
| 8371 | [this, CI](ElementCount VF) { |
| 8372 | return CM.isScalarWithPredication(CI, VF); |
| 8373 | }, |
| 8374 | Range); |
| 8375 | |
| 8376 | if (IsPredicated) |
| 8377 | return nullptr; |
| 8378 | |
| 8379 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 8380 | if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end || |
| 8381 | ID == Intrinsic::lifetime_start || ID == Intrinsic::sideeffect || |
| 8382 | ID == Intrinsic::pseudoprobe || |
| 8383 | ID == Intrinsic::experimental_noalias_scope_decl)) |
| 8384 | return nullptr; |
| 8385 | |
| 8386 | auto willWiden = [&](ElementCount VF) -> bool { |
| 8387 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 8388 | // The following case may be scalarized depending on the VF. |
| 8389 | // The flag shows whether we use Intrinsic or a usual Call for vectorized |
| 8390 | // version of the instruction. |
| 8391 | // Is it beneficial to perform intrinsic call compared to lib call? |
| 8392 | bool NeedToScalarize = false; |
| 8393 | InstructionCost CallCost = CM.getVectorCallCost(CI, VF, NeedToScalarize); |
| 8394 | InstructionCost IntrinsicCost = ID ? CM.getVectorIntrinsicCost(CI, VF) : 0; |
| 8395 | bool UseVectorIntrinsic = ID && IntrinsicCost <= CallCost; |
| 8396 | assert(IntrinsicCost.isValid() && CallCost.isValid() && |
| 8397 | "Cannot have invalid costs while widening" ); |
| 8398 | return UseVectorIntrinsic || !NeedToScalarize; |
| 8399 | }; |
| 8400 | |
| 8401 | if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range)) |
| 8402 | return nullptr; |
| 8403 | |
| 8404 | return new VPWidenCallRecipe(*CI, Plan.mapToVPValues(CI->arg_operands())); |
| 8405 | } |
| 8406 | |
| 8407 | bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const { |
| 8408 | assert(!isa<BranchInst>(I) && !isa<PHINode>(I) && !isa<LoadInst>(I) && |
| 8409 | !isa<StoreInst>(I) && "Instruction should have been handled earlier" ); |
| 8410 | // Instruction should be widened, unless it is scalar after vectorization, |
| 8411 | // scalarization is profitable or it is predicated. |
| 8412 | auto WillScalarize = [this, I](ElementCount VF) -> bool { |
| 8413 | return CM.isScalarAfterVectorization(I, VF) || |
| 8414 | CM.isProfitableToScalarize(I, VF) || |
| 8415 | CM.isScalarWithPredication(I, VF); |
| 8416 | }; |
| 8417 | return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize, |
| 8418 | Range); |
| 8419 | } |
| 8420 | |
| 8421 | VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I, VPlan &Plan) const { |
| 8422 | auto IsVectorizableOpcode = [](unsigned Opcode) { |
| 8423 | switch (Opcode) { |
| 8424 | case Instruction::Add: |
| 8425 | case Instruction::And: |
| 8426 | case Instruction::AShr: |
| 8427 | case Instruction::BitCast: |
| 8428 | case Instruction::FAdd: |
| 8429 | case Instruction::FCmp: |
| 8430 | case Instruction::FDiv: |
| 8431 | case Instruction::FMul: |
| 8432 | case Instruction::FNeg: |
| 8433 | case Instruction::FPExt: |
| 8434 | case Instruction::FPToSI: |
| 8435 | case Instruction::FPToUI: |
| 8436 | case Instruction::FPTrunc: |
| 8437 | case Instruction::FRem: |
| 8438 | case Instruction::FSub: |
| 8439 | case Instruction::ICmp: |
| 8440 | case Instruction::IntToPtr: |
| 8441 | case Instruction::LShr: |
| 8442 | case Instruction::Mul: |
| 8443 | case Instruction::Or: |
| 8444 | case Instruction::PtrToInt: |
| 8445 | case Instruction::SDiv: |
| 8446 | case Instruction::Select: |
| 8447 | case Instruction::SExt: |
| 8448 | case Instruction::Shl: |
| 8449 | case Instruction::SIToFP: |
| 8450 | case Instruction::SRem: |
| 8451 | case Instruction::Sub: |
| 8452 | case Instruction::Trunc: |
| 8453 | case Instruction::UDiv: |
| 8454 | case Instruction::UIToFP: |
| 8455 | case Instruction::URem: |
| 8456 | case Instruction::Xor: |
| 8457 | case Instruction::ZExt: |
| 8458 | return true; |
| 8459 | } |
| 8460 | return false; |
| 8461 | }; |
| 8462 | |
| 8463 | if (!IsVectorizableOpcode(I->getOpcode())) |
| 8464 | return nullptr; |
| 8465 | |
| 8466 | // Success: widen this instruction. |
| 8467 | return new VPWidenRecipe(*I, Plan.mapToVPValues(I->operands())); |
| 8468 | } |
| 8469 | |
| 8470 | VPBasicBlock *VPRecipeBuilder::handleReplication( |
| 8471 | Instruction *I, VFRange &Range, VPBasicBlock *VPBB, |
| 8472 | DenseMap<Instruction *, VPReplicateRecipe *> &PredInst2Recipe, |
| 8473 | VPlanPtr &Plan) { |
| 8474 | bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange( |
| 8475 | [&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); }, |
| 8476 | Range); |
| 8477 | |
| 8478 | bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( |
| 8479 | [&](ElementCount VF) { return CM.isScalarWithPredication(I, VF); }, |
| 8480 | Range); |
| 8481 | |
| 8482 | auto *Recipe = new VPReplicateRecipe(I, Plan->mapToVPValues(I->operands()), |
| 8483 | IsUniform, IsPredicated); |
| 8484 | setRecipe(I, Recipe); |
| 8485 | Plan->addVPValue(I, Recipe); |
| 8486 | |
| 8487 | // Find if I uses a predicated instruction. If so, it will use its scalar |
| 8488 | // value. Avoid hoisting the insert-element which packs the scalar value into |
| 8489 | // a vector value, as that happens iff all users use the vector value. |
| 8490 | for (auto &Op : I->operands()) |
| 8491 | if (auto *PredInst = dyn_cast<Instruction>(Op)) |
| 8492 | if (PredInst2Recipe.find(PredInst) != PredInst2Recipe.end()) |
| 8493 | PredInst2Recipe[PredInst]->setAlsoPack(false); |
| 8494 | |
| 8495 | // Finalize the recipe for Instr, first if it is not predicated. |
| 8496 | if (!IsPredicated) { |
| 8497 | LLVM_DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n" ); |
| 8498 | VPBB->appendRecipe(Recipe); |
| 8499 | return VPBB; |
| 8500 | } |
| 8501 | LLVM_DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n" ); |
| 8502 | assert(VPBB->getSuccessors().empty() && |
| 8503 | "VPBB has successors when handling predicated replication." ); |
| 8504 | // Record predicated instructions for above packing optimizations. |
| 8505 | PredInst2Recipe[I] = Recipe; |
| 8506 | VPBlockBase *Region = createReplicateRegion(I, Recipe, Plan); |
| 8507 | VPBlockUtils::insertBlockAfter(Region, VPBB); |
| 8508 | auto *RegSucc = new VPBasicBlock(); |
| 8509 | VPBlockUtils::insertBlockAfter(RegSucc, Region); |
| 8510 | return RegSucc; |
| 8511 | } |
| 8512 | |
| 8513 | VPRegionBlock *VPRecipeBuilder::createReplicateRegion(Instruction *Instr, |
| 8514 | VPRecipeBase *PredRecipe, |
| 8515 | VPlanPtr &Plan) { |
| 8516 | // Instructions marked for predication are replicated and placed under an |
| 8517 | // if-then construct to prevent side-effects. |
| 8518 | |
| 8519 | // Generate recipes to compute the block mask for this region. |
| 8520 | VPValue *BlockInMask = createBlockInMask(Instr->getParent(), Plan); |
| 8521 | |
| 8522 | // Build the triangular if-then region. |
| 8523 | std::string RegionName = (Twine("pred." ) + Instr->getOpcodeName()).str(); |
| 8524 | assert(Instr->getParent() && "Predicated instruction not in any basic block" ); |
| 8525 | auto *BOMRecipe = new VPBranchOnMaskRecipe(BlockInMask); |
| 8526 | auto *Entry = new VPBasicBlock(Twine(RegionName) + ".entry" , BOMRecipe); |
| 8527 | auto *PHIRecipe = Instr->getType()->isVoidTy() |
| 8528 | ? nullptr |
| 8529 | : new VPPredInstPHIRecipe(Plan->getOrAddVPValue(Instr)); |
| 8530 | auto *Exit = new VPBasicBlock(Twine(RegionName) + ".continue" , PHIRecipe); |
| 8531 | auto *Pred = new VPBasicBlock(Twine(RegionName) + ".if" , PredRecipe); |
| 8532 | VPRegionBlock *Region = new VPRegionBlock(Entry, Exit, RegionName, true); |
| 8533 | |
| 8534 | // Note: first set Entry as region entry and then connect successors starting |
| 8535 | // from it in order, to propagate the "parent" of each VPBasicBlock. |
| 8536 | VPBlockUtils::insertTwoBlocksAfter(Pred, Exit, BlockInMask, Entry); |
| 8537 | VPBlockUtils::connectBlocks(Pred, Exit); |
| 8538 | |
| 8539 | return Region; |
| 8540 | } |
| 8541 | |
| 8542 | VPRecipeBase *VPRecipeBuilder::tryToCreateWidenRecipe(Instruction *Instr, |
| 8543 | VFRange &Range, |
| 8544 | VPlanPtr &Plan) { |
| 8545 | // First, check for specific widening recipes that deal with calls, memory |
| 8546 | // operations, inductions and Phi nodes. |
| 8547 | if (auto *CI = dyn_cast<CallInst>(Instr)) |
| 8548 | return tryToWidenCall(CI, Range, *Plan); |
| 8549 | |
| 8550 | if (isa<LoadInst>(Instr) || isa<StoreInst>(Instr)) |
| 8551 | return tryToWidenMemory(Instr, Range, Plan); |
| 8552 | |
| 8553 | VPRecipeBase *Recipe; |
| 8554 | if (auto Phi = dyn_cast<PHINode>(Instr)) { |
| 8555 | if (Phi->getParent() != OrigLoop->getHeader()) |
| 8556 | return tryToBlend(Phi, Plan); |
| 8557 | if ((Recipe = tryToOptimizeInductionPHI(Phi, *Plan))) |
| 8558 | return Recipe; |
| 8559 | |
| 8560 | if (Legal->isReductionVariable(Phi)) { |
| 8561 | RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; |
| 8562 | VPValue *StartV = |
| 8563 | Plan->getOrAddVPValue(RdxDesc.getRecurrenceStartValue()); |
| 8564 | return new VPWidenPHIRecipe(Phi, RdxDesc, *StartV); |
| 8565 | } |
| 8566 | |
| 8567 | return new VPWidenPHIRecipe(Phi); |
| 8568 | } |
| 8569 | |
| 8570 | if (isa<TruncInst>(Instr) && (Recipe = tryToOptimizeInductionTruncate( |
| 8571 | cast<TruncInst>(Instr), Range, *Plan))) |
| 8572 | return Recipe; |
| 8573 | |
| 8574 | if (!shouldWiden(Instr, Range)) |
| 8575 | return nullptr; |
| 8576 | |
| 8577 | if (auto GEP = dyn_cast<GetElementPtrInst>(Instr)) |
| 8578 | return new VPWidenGEPRecipe(GEP, Plan->mapToVPValues(GEP->operands()), |
| 8579 | OrigLoop); |
| 8580 | |
| 8581 | if (auto *SI = dyn_cast<SelectInst>(Instr)) { |
| 8582 | bool InvariantCond = |
| 8583 | PSE.getSE()->isLoopInvariant(PSE.getSCEV(SI->getOperand(0)), OrigLoop); |
| 8584 | return new VPWidenSelectRecipe(*SI, Plan->mapToVPValues(SI->operands()), |
| 8585 | InvariantCond); |
| 8586 | } |
| 8587 | |
| 8588 | return tryToWiden(Instr, *Plan); |
| 8589 | } |
| 8590 | |
| 8591 | void LoopVectorizationPlanner::buildVPlansWithVPRecipes(ElementCount MinVF, |
| 8592 | ElementCount MaxVF) { |
| 8593 | assert(OrigLoop->isInnermost() && "Inner loop expected." ); |
| 8594 | |
| 8595 | // Collect instructions from the original loop that will become trivially dead |
| 8596 | // in the vectorized loop. We don't need to vectorize these instructions. For |
| 8597 | // example, original induction update instructions can become dead because we |
| 8598 | // separately emit induction "steps" when generating code for the new loop. |
| 8599 | // Similarly, we create a new latch condition when setting up the structure |
| 8600 | // of the new loop, so the old one can become dead. |
| 8601 | SmallPtrSet<Instruction *, 4> DeadInstructions; |
| 8602 | collectTriviallyDeadInstructions(DeadInstructions); |
| 8603 | |
| 8604 | // Add assume instructions we need to drop to DeadInstructions, to prevent |
| 8605 | // them from being added to the VPlan. |
| 8606 | // TODO: We only need to drop assumes in blocks that get flattend. If the |
| 8607 | // control flow is preserved, we should keep them. |
| 8608 | auto &ConditionalAssumes = Legal->getConditionalAssumes(); |
| 8609 | DeadInstructions.insert(ConditionalAssumes.begin(), ConditionalAssumes.end()); |
| 8610 | |
| 8611 | DenseMap<Instruction *, Instruction *> &SinkAfter = Legal->getSinkAfter(); |
| 8612 | // Dead instructions do not need sinking. Remove them from SinkAfter. |
| 8613 | for (Instruction *I : DeadInstructions) |
| 8614 | SinkAfter.erase(I); |
| 8615 | |
| 8616 | auto MaxVFPlusOne = MaxVF.getWithIncrement(1); |
| 8617 | for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFPlusOne);) { |
| 8618 | VFRange SubRange = {VF, MaxVFPlusOne}; |
| 8619 | VPlans.push_back( |
| 8620 | buildVPlanWithVPRecipes(SubRange, DeadInstructions, SinkAfter)); |
| 8621 | VF = SubRange.End; |
| 8622 | } |
| 8623 | } |
| 8624 | |
| 8625 | VPlanPtr LoopVectorizationPlanner::buildVPlanWithVPRecipes( |
| 8626 | VFRange &Range, SmallPtrSetImpl<Instruction *> &DeadInstructions, |
| 8627 | const DenseMap<Instruction *, Instruction *> &SinkAfter) { |
| 8628 | |
| 8629 | // Hold a mapping from predicated instructions to their recipes, in order to |
| 8630 | // fix their AlsoPack behavior if a user is determined to replicate and use a |
| 8631 | // scalar instead of vector value. |
| 8632 | DenseMap<Instruction *, VPReplicateRecipe *> PredInst2Recipe; |
| 8633 | |
| 8634 | SmallPtrSet<const InterleaveGroup<Instruction> *, 1> InterleaveGroups; |
| 8635 | |
| 8636 | VPRecipeBuilder RecipeBuilder(OrigLoop, TLI, Legal, CM, PSE, Builder); |
| 8637 | |
| 8638 | // --------------------------------------------------------------------------- |
| 8639 | // Pre-construction: record ingredients whose recipes we'll need to further |
| 8640 | // process after constructing the initial VPlan. |
| 8641 | // --------------------------------------------------------------------------- |
| 8642 | |
| 8643 | // Mark instructions we'll need to sink later and their targets as |
| 8644 | // ingredients whose recipe we'll need to record. |
| 8645 | for (auto &Entry : SinkAfter) { |
| 8646 | RecipeBuilder.recordRecipeOf(Entry.first); |
| 8647 | RecipeBuilder.recordRecipeOf(Entry.second); |
| 8648 | } |
| 8649 | for (auto &Reduction : CM.getInLoopReductionChains()) { |
| 8650 | PHINode *Phi = Reduction.first; |
| 8651 | RecurKind Kind = Legal->getReductionVars()[Phi].getRecurrenceKind(); |
| 8652 | const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second; |
| 8653 | |
| 8654 | RecipeBuilder.recordRecipeOf(Phi); |
| 8655 | for (auto &R : ReductionOperations) { |
| 8656 | RecipeBuilder.recordRecipeOf(R); |
| 8657 | // For min/max reducitons, where we have a pair of icmp/select, we also |
| 8658 | // need to record the ICmp recipe, so it can be removed later. |
| 8659 | if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) |
| 8660 | RecipeBuilder.recordRecipeOf(cast<Instruction>(R->getOperand(0))); |
| 8661 | } |
| 8662 | } |
| 8663 | |
| 8664 | // For each interleave group which is relevant for this (possibly trimmed) |
| 8665 | // Range, add it to the set of groups to be later applied to the VPlan and add |
| 8666 | // placeholders for its members' Recipes which we'll be replacing with a |
| 8667 | // single VPInterleaveRecipe. |
| 8668 | for (InterleaveGroup<Instruction> *IG : IAI.getInterleaveGroups()) { |
| 8669 | auto applyIG = [IG, this](ElementCount VF) -> bool { |
| 8670 | return (VF.isVector() && // Query is illegal for VF == 1 |
| 8671 | CM.getWideningDecision(IG->getInsertPos(), VF) == |
| 8672 | LoopVectorizationCostModel::CM_Interleave); |
| 8673 | }; |
| 8674 | if (!getDecisionAndClampRange(applyIG, Range)) |
| 8675 | continue; |
| 8676 | InterleaveGroups.insert(IG); |
| 8677 | for (unsigned i = 0; i < IG->getFactor(); i++) |
| 8678 | if (Instruction *Member = IG->getMember(i)) |
| 8679 | RecipeBuilder.recordRecipeOf(Member); |
| 8680 | }; |
| 8681 | |
| 8682 | // --------------------------------------------------------------------------- |
| 8683 | // Build initial VPlan: Scan the body of the loop in a topological order to |
| 8684 | // visit each basic block after having visited its predecessor basic blocks. |
| 8685 | // --------------------------------------------------------------------------- |
| 8686 | |
| 8687 | // Create a dummy pre-entry VPBasicBlock to start building the VPlan. |
| 8688 | auto Plan = std::make_unique<VPlan>(); |
| 8689 | VPBasicBlock *VPBB = new VPBasicBlock("Pre-Entry" ); |
| 8690 | Plan->setEntry(VPBB); |
| 8691 | |
| 8692 | // Scan the body of the loop in a topological order to visit each basic block |
| 8693 | // after having visited its predecessor basic blocks. |
| 8694 | LoopBlocksDFS DFS(OrigLoop); |
| 8695 | DFS.perform(LI); |
| 8696 | |
| 8697 | for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) { |
| 8698 | // Relevant instructions from basic block BB will be grouped into VPRecipe |
| 8699 | // ingredients and fill a new VPBasicBlock. |
| 8700 | unsigned VPBBsForBB = 0; |
| 8701 | auto *FirstVPBBForBB = new VPBasicBlock(BB->getName()); |
| 8702 | VPBlockUtils::insertBlockAfter(FirstVPBBForBB, VPBB); |
| 8703 | VPBB = FirstVPBBForBB; |
| 8704 | Builder.setInsertPoint(VPBB); |
| 8705 | |
| 8706 | // Introduce each ingredient into VPlan. |
| 8707 | // TODO: Model and preserve debug instrinsics in VPlan. |
| 8708 | for (Instruction &I : BB->instructionsWithoutDebug()) { |
| 8709 | Instruction *Instr = &I; |
| 8710 | |
| 8711 | // First filter out irrelevant instructions, to ensure no recipes are |
| 8712 | // built for them. |
| 8713 | if (isa<BranchInst>(Instr) || DeadInstructions.count(Instr)) |
| 8714 | continue; |
| 8715 | |
| 8716 | if (auto Recipe = |
| 8717 | RecipeBuilder.tryToCreateWidenRecipe(Instr, Range, Plan)) { |
| 8718 | for (auto *Def : Recipe->definedValues()) { |
| 8719 | auto *UV = Def->getUnderlyingValue(); |
| 8720 | Plan->addVPValue(UV, Def); |
| 8721 | } |
| 8722 | |
| 8723 | RecipeBuilder.setRecipe(Instr, Recipe); |
| 8724 | VPBB->appendRecipe(Recipe); |
| 8725 | continue; |
| 8726 | } |
| 8727 | |
| 8728 | // Otherwise, if all widening options failed, Instruction is to be |
| 8729 | // replicated. This may create a successor for VPBB. |
| 8730 | VPBasicBlock *NextVPBB = RecipeBuilder.handleReplication( |
| 8731 | Instr, Range, VPBB, PredInst2Recipe, Plan); |
| 8732 | if (NextVPBB != VPBB) { |
| 8733 | VPBB = NextVPBB; |
| 8734 | VPBB->setName(BB->hasName() ? BB->getName() + "." + Twine(VPBBsForBB++) |
| 8735 | : "" ); |
| 8736 | } |
| 8737 | } |
| 8738 | } |
| 8739 | |
| 8740 | // Discard empty dummy pre-entry VPBasicBlock. Note that other VPBasicBlocks |
| 8741 | // may also be empty, such as the last one VPBB, reflecting original |
| 8742 | // basic-blocks with no recipes. |
| 8743 | VPBasicBlock *PreEntry = cast<VPBasicBlock>(Plan->getEntry()); |
| 8744 | assert(PreEntry->empty() && "Expecting empty pre-entry block." ); |
| 8745 | VPBlockBase *Entry = Plan->setEntry(PreEntry->getSingleSuccessor()); |
| 8746 | VPBlockUtils::disconnectBlocks(PreEntry, Entry); |
| 8747 | delete PreEntry; |
| 8748 | |
| 8749 | // --------------------------------------------------------------------------- |
| 8750 | // Transform initial VPlan: Apply previously taken decisions, in order, to |
| 8751 | // bring the VPlan to its final state. |
| 8752 | // --------------------------------------------------------------------------- |
| 8753 | |
| 8754 | // Apply Sink-After legal constraints. |
| 8755 | for (auto &Entry : SinkAfter) { |
| 8756 | VPRecipeBase *Sink = RecipeBuilder.getRecipe(Entry.first); |
| 8757 | VPRecipeBase *Target = RecipeBuilder.getRecipe(Entry.second); |
| 8758 | // If the target is in a replication region, make sure to move Sink to the |
| 8759 | // block after it, not into the replication region itself. |
| 8760 | if (auto *Region = |
| 8761 | dyn_cast_or_null<VPRegionBlock>(Target->getParent()->getParent())) { |
| 8762 | if (Region->isReplicator()) { |
| 8763 | assert(Region->getNumSuccessors() == 1 && "Expected SESE region!" ); |
| 8764 | VPBasicBlock *NextBlock = |
| 8765 | cast<VPBasicBlock>(Region->getSuccessors().front()); |
| 8766 | Sink->moveBefore(*NextBlock, NextBlock->getFirstNonPhi()); |
| 8767 | continue; |
| 8768 | } |
| 8769 | } |
| 8770 | Sink->moveAfter(Target); |
| 8771 | } |
| 8772 | |
| 8773 | // Interleave memory: for each Interleave Group we marked earlier as relevant |
| 8774 | // for this VPlan, replace the Recipes widening its memory instructions with a |
| 8775 | // single VPInterleaveRecipe at its insertion point. |
| 8776 | for (auto IG : InterleaveGroups) { |
| 8777 | auto *Recipe = cast<VPWidenMemoryInstructionRecipe>( |
| 8778 | RecipeBuilder.getRecipe(IG->getInsertPos())); |
| 8779 | SmallVector<VPValue *, 4> StoredValues; |
| 8780 | for (unsigned i = 0; i < IG->getFactor(); ++i) |
| 8781 | if (auto *SI = dyn_cast_or_null<StoreInst>(IG->getMember(i))) |
| 8782 | StoredValues.push_back(Plan->getOrAddVPValue(SI->getOperand(0))); |
| 8783 | |
| 8784 | auto *VPIG = new VPInterleaveRecipe(IG, Recipe->getAddr(), StoredValues, |
| 8785 | Recipe->getMask()); |
| 8786 | VPIG->insertBefore(Recipe); |
| 8787 | unsigned J = 0; |
| 8788 | for (unsigned i = 0; i < IG->getFactor(); ++i) |
| 8789 | if (Instruction *Member = IG->getMember(i)) { |
| 8790 | if (!Member->getType()->isVoidTy()) { |
| 8791 | VPValue *OriginalV = Plan->getVPValue(Member); |
| 8792 | Plan->removeVPValueFor(Member); |
| 8793 | Plan->addVPValue(Member, VPIG->getVPValue(J)); |
| 8794 | OriginalV->replaceAllUsesWith(VPIG->getVPValue(J)); |
| 8795 | J++; |
| 8796 | } |
| 8797 | RecipeBuilder.getRecipe(Member)->eraseFromParent(); |
| 8798 | } |
| 8799 | } |
| 8800 | |
| 8801 | // Adjust the recipes for any inloop reductions. |
| 8802 | if (Range.Start.isVector()) |
| 8803 | adjustRecipesForInLoopReductions(Plan, RecipeBuilder); |
| 8804 | |
| 8805 | // Finally, if tail is folded by masking, introduce selects between the phi |
| 8806 | // and the live-out instruction of each reduction, at the end of the latch. |
| 8807 | if (CM.foldTailByMasking() && !Legal->getReductionVars().empty()) { |
| 8808 | Builder.setInsertPoint(VPBB); |
| 8809 | auto *Cond = RecipeBuilder.createBlockInMask(OrigLoop->getHeader(), Plan); |
| 8810 | for (auto &Reduction : Legal->getReductionVars()) { |
| 8811 | if (CM.isInLoopReduction(Reduction.first)) |
| 8812 | continue; |
| 8813 | VPValue *Phi = Plan->getOrAddVPValue(Reduction.first); |
| 8814 | VPValue *Red = Plan->getOrAddVPValue(Reduction.second.getLoopExitInstr()); |
| 8815 | Builder.createNaryOp(Instruction::Select, {Cond, Red, Phi}); |
| 8816 | } |
| 8817 | } |
| 8818 | |
| 8819 | std::string PlanName; |
| 8820 | raw_string_ostream RSO(PlanName); |
| 8821 | ElementCount VF = Range.Start; |
| 8822 | Plan->addVF(VF); |
| 8823 | RSO << "Initial VPlan for VF={" << VF; |
| 8824 | for (VF *= 2; ElementCount::isKnownLT(VF, Range.End); VF *= 2) { |
| 8825 | Plan->addVF(VF); |
| 8826 | RSO << "," << VF; |
| 8827 | } |
| 8828 | RSO << "},UF>=1" ; |
| 8829 | RSO.flush(); |
| 8830 | Plan->setName(PlanName); |
| 8831 | |
| 8832 | return Plan; |
| 8833 | } |
| 8834 | |
| 8835 | VPlanPtr LoopVectorizationPlanner::buildVPlan(VFRange &Range) { |
| 8836 | // Outer loop handling: They may require CFG and instruction level |
| 8837 | // transformations before even evaluating whether vectorization is profitable. |
| 8838 | // Since we cannot modify the incoming IR, we need to build VPlan upfront in |
| 8839 | // the vectorization pipeline. |
| 8840 | assert(!OrigLoop->isInnermost()); |
| 8841 | assert(EnableVPlanNativePath && "VPlan-native path is not enabled." ); |
| 8842 | |
| 8843 | // Create new empty VPlan |
| 8844 | auto Plan = std::make_unique<VPlan>(); |
| 8845 | |
| 8846 | // Build hierarchical CFG |
| 8847 | VPlanHCFGBuilder HCFGBuilder(OrigLoop, LI, *Plan); |
| 8848 | HCFGBuilder.buildHierarchicalCFG(); |
| 8849 | |
| 8850 | for (ElementCount VF = Range.Start; ElementCount::isKnownLT(VF, Range.End); |
| 8851 | VF *= 2) |
| 8852 | Plan->addVF(VF); |
| 8853 | |
| 8854 | if (EnableVPlanPredication) { |
| 8855 | VPlanPredicator VPP(*Plan); |
| 8856 | VPP.predicate(); |
| 8857 | |
| 8858 | // Avoid running transformation to recipes until masked code generation in |
| 8859 | // VPlan-native path is in place. |
| 8860 | return Plan; |
| 8861 | } |
| 8862 | |
| 8863 | SmallPtrSet<Instruction *, 1> DeadInstructions; |
| 8864 | VPlanTransforms::VPInstructionsToVPRecipes( |
| 8865 | OrigLoop, Plan, Legal->getInductionVars(), DeadInstructions); |
| 8866 | return Plan; |
| 8867 | } |
| 8868 | |
| 8869 | // Adjust the recipes for any inloop reductions. The chain of instructions |
| 8870 | // leading from the loop exit instr to the phi need to be converted to |
| 8871 | // reductions, with one operand being vector and the other being the scalar |
| 8872 | // reduction chain. |
| 8873 | void LoopVectorizationPlanner::adjustRecipesForInLoopReductions( |
| 8874 | VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder) { |
| 8875 | for (auto &Reduction : CM.getInLoopReductionChains()) { |
| 8876 | PHINode *Phi = Reduction.first; |
| 8877 | RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; |
| 8878 | const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second; |
| 8879 | |
| 8880 | // ReductionOperations are orders top-down from the phi's use to the |
| 8881 | // LoopExitValue. We keep a track of the previous item (the Chain) to tell |
| 8882 | // which of the two operands will remain scalar and which will be reduced. |
| 8883 | // For minmax the chain will be the select instructions. |
| 8884 | Instruction *Chain = Phi; |
| 8885 | for (Instruction *R : ReductionOperations) { |
| 8886 | VPRecipeBase *WidenRecipe = RecipeBuilder.getRecipe(R); |
| 8887 | RecurKind Kind = RdxDesc.getRecurrenceKind(); |
| 8888 | |
| 8889 | VPValue *ChainOp = Plan->getVPValue(Chain); |
| 8890 | unsigned FirstOpId; |
| 8891 | if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| 8892 | assert(isa<VPWidenSelectRecipe>(WidenRecipe) && |
| 8893 | "Expected to replace a VPWidenSelectSC" ); |
| 8894 | FirstOpId = 1; |
| 8895 | } else { |
| 8896 | assert(isa<VPWidenRecipe>(WidenRecipe) && |
| 8897 | "Expected to replace a VPWidenSC" ); |
| 8898 | FirstOpId = 0; |
| 8899 | } |
| 8900 | unsigned VecOpId = |
| 8901 | R->getOperand(FirstOpId) == Chain ? FirstOpId + 1 : FirstOpId; |
| 8902 | VPValue *VecOp = Plan->getVPValue(R->getOperand(VecOpId)); |
| 8903 | |
| 8904 | auto *CondOp = CM.foldTailByMasking() |
| 8905 | ? RecipeBuilder.createBlockInMask(R->getParent(), Plan) |
| 8906 | : nullptr; |
| 8907 | VPReductionRecipe *RedRecipe = new VPReductionRecipe( |
| 8908 | &RdxDesc, R, ChainOp, VecOp, CondOp, Legal->hasFunNoNaNAttr(), TTI); |
| 8909 | WidenRecipe->getVPValue()->replaceAllUsesWith(RedRecipe); |
| 8910 | Plan->removeVPValueFor(R); |
| 8911 | Plan->addVPValue(R, RedRecipe); |
| 8912 | WidenRecipe->getParent()->insert(RedRecipe, WidenRecipe->getIterator()); |
| 8913 | WidenRecipe->getVPValue()->replaceAllUsesWith(RedRecipe); |
| 8914 | WidenRecipe->eraseFromParent(); |
| 8915 | |
| 8916 | if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| 8917 | VPRecipeBase *CompareRecipe = |
| 8918 | RecipeBuilder.getRecipe(cast<Instruction>(R->getOperand(0))); |
| 8919 | assert(isa<VPWidenRecipe>(CompareRecipe) && |
| 8920 | "Expected to replace a VPWidenSC" ); |
| 8921 | assert(cast<VPWidenRecipe>(CompareRecipe)->getNumUsers() == 0 && |
| 8922 | "Expected no remaining users" ); |
| 8923 | CompareRecipe->eraseFromParent(); |
| 8924 | } |
| 8925 | Chain = R; |
| 8926 | } |
| 8927 | } |
| 8928 | } |
| 8929 | |
| 8930 | Value* LoopVectorizationPlanner::VPCallbackILV:: |
| 8931 | getOrCreateVectorValues(Value *V, unsigned Part) { |
| 8932 | return ILV.getOrCreateVectorValue(V, Part); |
| 8933 | } |
| 8934 | |
| 8935 | Value *LoopVectorizationPlanner::VPCallbackILV::getOrCreateScalarValue( |
| 8936 | Value *V, const VPIteration &Instance) { |
| 8937 | return ILV.getOrCreateScalarValue(V, Instance); |
| 8938 | } |
| 8939 | |
| 8940 | void VPInterleaveRecipe::print(raw_ostream &O, const Twine &Indent, |
| 8941 | VPSlotTracker &SlotTracker) const { |
| 8942 | O << "\"INTERLEAVE-GROUP with factor " << IG->getFactor() << " at " ; |
| 8943 | IG->getInsertPos()->printAsOperand(O, false); |
| 8944 | O << ", " ; |
| 8945 | getAddr()->printAsOperand(O, SlotTracker); |
| 8946 | VPValue *Mask = getMask(); |
| 8947 | if (Mask) { |
| 8948 | O << ", " ; |
| 8949 | Mask->printAsOperand(O, SlotTracker); |
| 8950 | } |
| 8951 | for (unsigned i = 0; i < IG->getFactor(); ++i) |
| 8952 | if (Instruction *I = IG->getMember(i)) |
| 8953 | O << "\\l\" +\n" << Indent << "\" " << VPlanIngredient(I) << " " << i; |
| 8954 | } |
| 8955 | |
| 8956 | void VPWidenCallRecipe::execute(VPTransformState &State) { |
| 8957 | State.ILV->widenCallInstruction(*cast<CallInst>(getUnderlyingInstr()), this, |
| 8958 | *this, State); |
| 8959 | } |
| 8960 | |
| 8961 | void VPWidenSelectRecipe::execute(VPTransformState &State) { |
| 8962 | State.ILV->widenSelectInstruction(*cast<SelectInst>(getUnderlyingInstr()), |
| 8963 | this, *this, InvariantCond, State); |
| 8964 | } |
| 8965 | |
| 8966 | void VPWidenRecipe::execute(VPTransformState &State) { |
| 8967 | State.ILV->widenInstruction(*getUnderlyingInstr(), this, *this, State); |
| 8968 | } |
| 8969 | |
| 8970 | void VPWidenGEPRecipe::execute(VPTransformState &State) { |
| 8971 | State.ILV->widenGEP(cast<GetElementPtrInst>(getUnderlyingInstr()), this, |
| 8972 | *this, State.UF, State.VF, IsPtrLoopInvariant, |
| 8973 | IsIndexLoopInvariant, State); |
| 8974 | } |
| 8975 | |
| 8976 | void VPWidenIntOrFpInductionRecipe::execute(VPTransformState &State) { |
| 8977 | assert(!State.Instance && "Int or FP induction being replicated." ); |
| 8978 | State.ILV->widenIntOrFpInduction(IV, getStartValue()->getLiveInIRValue(), |
| 8979 | Trunc); |
| 8980 | } |
| 8981 | |
| 8982 | void VPWidenPHIRecipe::execute(VPTransformState &State) { |
| 8983 | Value *StartV = |
| 8984 | getStartValue() ? getStartValue()->getLiveInIRValue() : nullptr; |
| 8985 | State.ILV->widenPHIInstruction(Phi, RdxDesc, StartV, State.UF, State.VF); |
| 8986 | } |
| 8987 | |
| 8988 | void VPBlendRecipe::execute(VPTransformState &State) { |
| 8989 | State.ILV->setDebugLocFromInst(State.Builder, Phi); |
| 8990 | // We know that all PHIs in non-header blocks are converted into |
| 8991 | // selects, so we don't have to worry about the insertion order and we |
| 8992 | // can just use the builder. |
| 8993 | // At this point we generate the predication tree. There may be |
| 8994 | // duplications since this is a simple recursive scan, but future |
| 8995 | // optimizations will clean it up. |
| 8996 | |
| 8997 | unsigned NumIncoming = getNumIncomingValues(); |
| 8998 | |
| 8999 | // Generate a sequence of selects of the form: |
| 9000 | // SELECT(Mask3, In3, |
| 9001 | // SELECT(Mask2, In2, |
| 9002 | // SELECT(Mask1, In1, |
| 9003 | // In0))) |
| 9004 | // Note that Mask0 is never used: lanes for which no path reaches this phi and |
| 9005 | // are essentially undef are taken from In0. |
| 9006 | InnerLoopVectorizer::VectorParts Entry(State.UF); |
| 9007 | for (unsigned In = 0; In < NumIncoming; ++In) { |
| 9008 | for (unsigned Part = 0; Part < State.UF; ++Part) { |
| 9009 | // We might have single edge PHIs (blocks) - use an identity |
| 9010 | // 'select' for the first PHI operand. |
| 9011 | Value *In0 = State.get(getIncomingValue(In), Part); |
| 9012 | if (In == 0) |
| 9013 | Entry[Part] = In0; // Initialize with the first incoming value. |
| 9014 | else { |
| 9015 | // Select between the current value and the previous incoming edge |
| 9016 | // based on the incoming mask. |
| 9017 | Value *Cond = State.get(getMask(In), Part); |
| 9018 | Entry[Part] = |
| 9019 | State.Builder.CreateSelect(Cond, In0, Entry[Part], "predphi" ); |
| 9020 | } |
| 9021 | } |
| 9022 | } |
| 9023 | for (unsigned Part = 0; Part < State.UF; ++Part) |
| 9024 | State.ValueMap.setVectorValue(Phi, Part, Entry[Part]); |
| 9025 | } |
| 9026 | |
| 9027 | void VPInterleaveRecipe::execute(VPTransformState &State) { |
| 9028 | assert(!State.Instance && "Interleave group being replicated." ); |
| 9029 | State.ILV->vectorizeInterleaveGroup(IG, definedValues(), State, getAddr(), |
| 9030 | getStoredValues(), getMask()); |
| 9031 | } |
| 9032 | |
| 9033 | void VPReductionRecipe::execute(VPTransformState &State) { |
| 9034 | assert(!State.Instance && "Reduction being replicated." ); |
| 9035 | for (unsigned Part = 0; Part < State.UF; ++Part) { |
| 9036 | RecurKind Kind = RdxDesc->getRecurrenceKind(); |
| 9037 | Value *NewVecOp = State.get(getVecOp(), Part); |
| 9038 | if (VPValue *Cond = getCondOp()) { |
| 9039 | Value *NewCond = State.get(Cond, Part); |
| 9040 | VectorType *VecTy = cast<VectorType>(NewVecOp->getType()); |
| 9041 | Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity( |
| 9042 | Kind, VecTy->getElementType()); |
| 9043 | Constant *IdenVec = |
| 9044 | ConstantVector::getSplat(VecTy->getElementCount(), Iden); |
| 9045 | Value *Select = State.Builder.CreateSelect(NewCond, NewVecOp, IdenVec); |
| 9046 | NewVecOp = Select; |
| 9047 | } |
| 9048 | Value *NewRed = |
| 9049 | createTargetReduction(State.Builder, TTI, *RdxDesc, NewVecOp); |
| 9050 | Value *PrevInChain = State.get(getChainOp(), Part); |
| 9051 | Value *NextInChain; |
| 9052 | if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| 9053 | NextInChain = |
| 9054 | createMinMaxOp(State.Builder, RdxDesc->getRecurrenceKind(), |
| 9055 | NewRed, PrevInChain); |
| 9056 | } else { |
| 9057 | NextInChain = State.Builder.CreateBinOp( |
| 9058 | (Instruction::BinaryOps)getUnderlyingInstr()->getOpcode(), NewRed, |
| 9059 | PrevInChain); |
| 9060 | } |
| 9061 | State.set(this, getUnderlyingInstr(), NextInChain, Part); |
| 9062 | } |
| 9063 | } |
| 9064 | |
| 9065 | void VPReplicateRecipe::execute(VPTransformState &State) { |
| 9066 | if (State.Instance) { // Generate a single instance. |
| 9067 | assert(!State.VF.isScalable() && "Can't scalarize a scalable vector" ); |
| 9068 | State.ILV->scalarizeInstruction(getUnderlyingInstr(), *this, |
| 9069 | *State.Instance, IsPredicated, State); |
| 9070 | // Insert scalar instance packing it into a vector. |
| 9071 | if (AlsoPack && State.VF.isVector()) { |
| 9072 | // If we're constructing lane 0, initialize to start from poison. |
| 9073 | if (State.Instance->Lane == 0) { |
| 9074 | assert(!State.VF.isScalable() && "VF is assumed to be non scalable." ); |
| 9075 | Value *Poison = PoisonValue::get( |
| 9076 | VectorType::get(getUnderlyingValue()->getType(), State.VF)); |
| 9077 | State.ValueMap.setVectorValue(getUnderlyingInstr(), |
| 9078 | State.Instance->Part, Poison); |
| 9079 | } |
| 9080 | State.ILV->packScalarIntoVectorValue(getUnderlyingInstr(), |
| 9081 | *State.Instance); |
| 9082 | } |
| 9083 | return; |
| 9084 | } |
| 9085 | |
| 9086 | // Generate scalar instances for all VF lanes of all UF parts, unless the |
| 9087 | // instruction is uniform inwhich case generate only the first lane for each |
| 9088 | // of the UF parts. |
| 9089 | unsigned EndLane = IsUniform ? 1 : State.VF.getKnownMinValue(); |
| 9090 | assert((!State.VF.isScalable() || IsUniform) && |
| 9091 | "Can't scalarize a scalable vector" ); |
| 9092 | for (unsigned Part = 0; Part < State.UF; ++Part) |
| 9093 | for (unsigned Lane = 0; Lane < EndLane; ++Lane) |
| 9094 | State.ILV->scalarizeInstruction(getUnderlyingInstr(), *this, {Part, Lane}, |
| 9095 | IsPredicated, State); |
| 9096 | } |
| 9097 | |
| 9098 | void VPBranchOnMaskRecipe::execute(VPTransformState &State) { |
| 9099 | assert(State.Instance && "Branch on Mask works only on single instance." ); |
| 9100 | |
| 9101 | unsigned Part = State.Instance->Part; |
| 9102 | unsigned Lane = State.Instance->Lane; |
| 9103 | |
| 9104 | Value *ConditionBit = nullptr; |
| 9105 | VPValue *BlockInMask = getMask(); |
| 9106 | if (BlockInMask) { |
| 9107 | ConditionBit = State.get(BlockInMask, Part); |
| 9108 | if (ConditionBit->getType()->isVectorTy()) |
| 9109 | ConditionBit = State.Builder.CreateExtractElement( |
| 9110 | ConditionBit, State.Builder.getInt32(Lane)); |
| 9111 | } else // Block in mask is all-one. |
| 9112 | ConditionBit = State.Builder.getTrue(); |
| 9113 | |
| 9114 | // Replace the temporary unreachable terminator with a new conditional branch, |
| 9115 | // whose two destinations will be set later when they are created. |
| 9116 | auto *CurrentTerminator = State.CFG.PrevBB->getTerminator(); |
| 9117 | assert(isa<UnreachableInst>(CurrentTerminator) && |
| 9118 | "Expected to replace unreachable terminator with conditional branch." ); |
| 9119 | auto *CondBr = BranchInst::Create(State.CFG.PrevBB, nullptr, ConditionBit); |
| 9120 | CondBr->setSuccessor(0, nullptr); |
| 9121 | ReplaceInstWithInst(CurrentTerminator, CondBr); |
| 9122 | } |
| 9123 | |
| 9124 | void VPPredInstPHIRecipe::execute(VPTransformState &State) { |
| 9125 | assert(State.Instance && "Predicated instruction PHI works per instance." ); |
| 9126 | Instruction *ScalarPredInst = |
| 9127 | cast<Instruction>(State.get(getOperand(0), *State.Instance)); |
| 9128 | BasicBlock *PredicatedBB = ScalarPredInst->getParent(); |
| 9129 | BasicBlock *PredicatingBB = PredicatedBB->getSinglePredecessor(); |
| 9130 | assert(PredicatingBB && "Predicated block has no single predecessor." ); |
| 9131 | |
| 9132 | // By current pack/unpack logic we need to generate only a single phi node: if |
| 9133 | // a vector value for the predicated instruction exists at this point it means |
| 9134 | // the instruction has vector users only, and a phi for the vector value is |
| 9135 | // needed. In this case the recipe of the predicated instruction is marked to |
| 9136 | // also do that packing, thereby "hoisting" the insert-element sequence. |
| 9137 | // Otherwise, a phi node for the scalar value is needed. |
| 9138 | unsigned Part = State.Instance->Part; |
| 9139 | Instruction *PredInst = |
| 9140 | cast<Instruction>(getOperand(0)->getUnderlyingValue()); |
| 9141 | if (State.ValueMap.hasVectorValue(PredInst, Part)) { |
| 9142 | Value *VectorValue = State.ValueMap.getVectorValue(PredInst, Part); |
| 9143 | InsertElementInst *IEI = cast<InsertElementInst>(VectorValue); |
| 9144 | PHINode *VPhi = State.Builder.CreatePHI(IEI->getType(), 2); |
| 9145 | VPhi->addIncoming(IEI->getOperand(0), PredicatingBB); // Unmodified vector. |
| 9146 | VPhi->addIncoming(IEI, PredicatedBB); // New vector with inserted element. |
| 9147 | State.ValueMap.resetVectorValue(PredInst, Part, VPhi); // Update cache. |
| 9148 | } else { |
| 9149 | Type *PredInstType = PredInst->getType(); |
| 9150 | PHINode *Phi = State.Builder.CreatePHI(PredInstType, 2); |
| 9151 | Phi->addIncoming(PoisonValue::get(ScalarPredInst->getType()), PredicatingBB); |
| 9152 | Phi->addIncoming(ScalarPredInst, PredicatedBB); |
| 9153 | State.ValueMap.resetScalarValue(PredInst, *State.Instance, Phi); |
| 9154 | } |
| 9155 | } |
| 9156 | |
| 9157 | void VPWidenMemoryInstructionRecipe::execute(VPTransformState &State) { |
| 9158 | VPValue *StoredValue = isStore() ? getStoredValue() : nullptr; |
| 9159 | State.ILV->vectorizeMemoryInstruction(&Ingredient, State, |
| 9160 | StoredValue ? nullptr : getVPValue(), |
| 9161 | getAddr(), StoredValue, getMask()); |
| 9162 | } |
| 9163 | |
| 9164 | // Determine how to lower the scalar epilogue, which depends on 1) optimising |
| 9165 | // for minimum code-size, 2) predicate compiler options, 3) loop hints forcing |
| 9166 | // predication, and 4) a TTI hook that analyses whether the loop is suitable |
| 9167 | // for predication. |
| 9168 | static ScalarEpilogueLowering getScalarEpilogueLowering( |
| 9169 | Function *F, Loop *L, LoopVectorizeHints &Hints, ProfileSummaryInfo *PSI, |
| 9170 | BlockFrequencyInfo *BFI, TargetTransformInfo *TTI, TargetLibraryInfo *TLI, |
| 9171 | AssumptionCache *AC, LoopInfo *LI, ScalarEvolution *SE, DominatorTree *DT, |
| 9172 | LoopVectorizationLegality &LVL) { |
| 9173 | // 1) OptSize takes precedence over all other options, i.e. if this is set, |
| 9174 | // don't look at hints or options, and don't request a scalar epilogue. |
| 9175 | // (For PGSO, as shouldOptimizeForSize isn't currently accessible from |
| 9176 | // LoopAccessInfo (due to code dependency and not being able to reliably get |
| 9177 | // PSI/BFI from a loop analysis under NPM), we cannot suppress the collection |
| 9178 | // of strides in LoopAccessInfo::analyzeLoop() and vectorize without |
| 9179 | // versioning when the vectorization is forced, unlike hasOptSize. So revert |
| 9180 | // back to the old way and vectorize with versioning when forced. See D81345.) |
| 9181 | if (F->hasOptSize() || (llvm::shouldOptimizeForSize(L->getHeader(), PSI, BFI, |
| 9182 | PGSOQueryType::IRPass) && |
| 9183 | Hints.getForce() != LoopVectorizeHints::FK_Enabled)) |
| 9184 | return CM_ScalarEpilogueNotAllowedOptSize; |
| 9185 | |
| 9186 | // 2) If set, obey the directives |
| 9187 | if (PreferPredicateOverEpilogue.getNumOccurrences()) { |
| 9188 | switch (PreferPredicateOverEpilogue) { |
| 9189 | case PreferPredicateTy::ScalarEpilogue: |
| 9190 | return CM_ScalarEpilogueAllowed; |
| 9191 | case PreferPredicateTy::PredicateElseScalarEpilogue: |
| 9192 | return CM_ScalarEpilogueNotNeededUsePredicate; |
| 9193 | case PreferPredicateTy::PredicateOrDontVectorize: |
| 9194 | return CM_ScalarEpilogueNotAllowedUsePredicate; |
| 9195 | }; |
| 9196 | } |
| 9197 | |
| 9198 | // 3) If set, obey the hints |
| 9199 | switch (Hints.getPredicate()) { |
| 9200 | case LoopVectorizeHints::FK_Enabled: |
| 9201 | return CM_ScalarEpilogueNotNeededUsePredicate; |
| 9202 | case LoopVectorizeHints::FK_Disabled: |
| 9203 | return CM_ScalarEpilogueAllowed; |
| 9204 | }; |
| 9205 | |
| 9206 | // 4) if the TTI hook indicates this is profitable, request predication. |
| 9207 | if (TTI->preferPredicateOverEpilogue(L, LI, *SE, *AC, TLI, DT, |
| 9208 | LVL.getLAI())) |
| 9209 | return CM_ScalarEpilogueNotNeededUsePredicate; |
| 9210 | |
| 9211 | return CM_ScalarEpilogueAllowed; |
| 9212 | } |
| 9213 | |
| 9214 | void VPTransformState::set(VPValue *Def, Value *IRDef, Value *V, |
| 9215 | unsigned Part) { |
| 9216 | set(Def, V, Part); |
| 9217 | ILV->setVectorValue(IRDef, Part, V); |
| 9218 | } |
| 9219 | |
| 9220 | // Process the loop in the VPlan-native vectorization path. This path builds |
| 9221 | // VPlan upfront in the vectorization pipeline, which allows to apply |
| 9222 | // VPlan-to-VPlan transformations from the very beginning without modifying the |
| 9223 | // input LLVM IR. |
| 9224 | static bool processLoopInVPlanNativePath( |
| 9225 | Loop *L, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT, |
| 9226 | LoopVectorizationLegality *LVL, TargetTransformInfo *TTI, |
| 9227 | TargetLibraryInfo *TLI, DemandedBits *DB, AssumptionCache *AC, |
| 9228 | OptimizationRemarkEmitter *ORE, BlockFrequencyInfo *BFI, |
| 9229 | ProfileSummaryInfo *PSI, LoopVectorizeHints &Hints) { |
| 9230 | |
| 9231 | if (isa<SCEVCouldNotCompute>(PSE.getBackedgeTakenCount())) { |
| 9232 | LLVM_DEBUG(dbgs() << "LV: cannot compute the outer-loop trip count\n" ); |
| 9233 | return false; |
| 9234 | } |
| 9235 | assert(EnableVPlanNativePath && "VPlan-native path is disabled." ); |
| 9236 | Function *F = L->getHeader()->getParent(); |
| 9237 | InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL->getLAI()); |
| 9238 | |
| 9239 | ScalarEpilogueLowering SEL = getScalarEpilogueLowering( |
| 9240 | F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, *LVL); |
| 9241 | |
| 9242 | LoopVectorizationCostModel CM(SEL, L, PSE, LI, LVL, *TTI, TLI, DB, AC, ORE, F, |
| 9243 | &Hints, IAI); |
| 9244 | // Use the planner for outer loop vectorization. |
| 9245 | // TODO: CM is not used at this point inside the planner. Turn CM into an |
| 9246 | // optional argument if we don't need it in the future. |
| 9247 | LoopVectorizationPlanner LVP(L, LI, TLI, TTI, LVL, CM, IAI, PSE); |
| 9248 | |
| 9249 | // Get user vectorization factor. |
| 9250 | ElementCount UserVF = Hints.getWidth(); |
| 9251 | |
| 9252 | // Plan how to best vectorize, return the best VF and its cost. |
| 9253 | const VectorizationFactor VF = LVP.planInVPlanNativePath(UserVF); |
| 9254 | |
| 9255 | // If we are stress testing VPlan builds, do not attempt to generate vector |
| 9256 | // code. Masked vector code generation support will follow soon. |
| 9257 | // Also, do not attempt to vectorize if no vector code will be produced. |
| 9258 | if (VPlanBuildStressTest || EnableVPlanPredication || |
| 9259 | VectorizationFactor::Disabled() == VF) |
| 9260 | return false; |
| 9261 | |
| 9262 | LVP.setBestPlan(VF.Width, 1); |
| 9263 | |
| 9264 | InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, 1, LVL, |
| 9265 | &CM, BFI, PSI); |
| 9266 | LLVM_DEBUG(dbgs() << "Vectorizing outer loop in \"" |
| 9267 | << L->getHeader()->getParent()->getName() << "\"\n" ); |
| 9268 | LVP.executePlan(LB, DT); |
| 9269 | |
| 9270 | // Mark the loop as already vectorized to avoid vectorizing again. |
| 9271 | Hints.setAlreadyVectorized(); |
| 9272 | |
| 9273 | assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs())); |
| 9274 | return true; |
| 9275 | } |
| 9276 | |
| 9277 | LoopVectorizePass::LoopVectorizePass(LoopVectorizeOptions Opts) |
| 9278 | : InterleaveOnlyWhenForced(Opts.InterleaveOnlyWhenForced || |
| 9279 | !EnableLoopInterleaving), |
| 9280 | VectorizeOnlyWhenForced(Opts.VectorizeOnlyWhenForced || |
| 9281 | !EnableLoopVectorization) {} |
| 9282 | |
| 9283 | bool LoopVectorizePass::processLoop(Loop *L) { |
| 9284 | assert((EnableVPlanNativePath || L->isInnermost()) && |
| 9285 | "VPlan-native path is not enabled. Only process inner loops." ); |
| 9286 | |
| 9287 | #ifndef NDEBUG |
| 9288 | const std::string DebugLocStr = getDebugLocString(L); |
| 9289 | #endif /* NDEBUG */ |
| 9290 | |
| 9291 | LLVM_DEBUG(dbgs() << "\nLV: Checking a loop in \"" |
| 9292 | << L->getHeader()->getParent()->getName() << "\" from " |
| 9293 | << DebugLocStr << "\n" ); |
| 9294 | |
| 9295 | LoopVectorizeHints Hints(L, InterleaveOnlyWhenForced, *ORE); |
| 9296 | |
| 9297 | LLVM_DEBUG( |
| 9298 | dbgs() << "LV: Loop hints:" |
| 9299 | << " force=" |
| 9300 | << (Hints.getForce() == LoopVectorizeHints::FK_Disabled |
| 9301 | ? "disabled" |
| 9302 | : (Hints.getForce() == LoopVectorizeHints::FK_Enabled |
| 9303 | ? "enabled" |
| 9304 | : "?" )) |
| 9305 | << " width=" << Hints.getWidth() |
| 9306 | << " unroll=" << Hints.getInterleave() << "\n" ); |
| 9307 | |
| 9308 | // Function containing loop |
| 9309 | Function *F = L->getHeader()->getParent(); |
| 9310 | |
| 9311 | // Looking at the diagnostic output is the only way to determine if a loop |
| 9312 | // was vectorized (other than looking at the IR or machine code), so it |
| 9313 | // is important to generate an optimization remark for each loop. Most of |
| 9314 | // these messages are generated as OptimizationRemarkAnalysis. Remarks |
| 9315 | // generated as OptimizationRemark and OptimizationRemarkMissed are |
| 9316 | // less verbose reporting vectorized loops and unvectorized loops that may |
| 9317 | // benefit from vectorization, respectively. |
| 9318 | |
| 9319 | if (!Hints.allowVectorization(F, L, VectorizeOnlyWhenForced)) { |
| 9320 | LLVM_DEBUG(dbgs() << "LV: Loop hints prevent vectorization.\n" ); |
| 9321 | return false; |
| 9322 | } |
| 9323 | |
| 9324 | PredicatedScalarEvolution PSE(*SE, *L); |
| 9325 | |
| 9326 | // Check if it is legal to vectorize the loop. |
| 9327 | LoopVectorizationRequirements Requirements(*ORE); |
| 9328 | LoopVectorizationLegality LVL(L, PSE, DT, TTI, TLI, AA, F, GetLAA, LI, ORE, |
| 9329 | &Requirements, &Hints, DB, AC, BFI, PSI); |
| 9330 | if (!LVL.canVectorize(EnableVPlanNativePath)) { |
| 9331 | LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n" ); |
| 9332 | Hints.emitRemarkWithHints(); |
| 9333 | return false; |
| 9334 | } |
| 9335 | |
| 9336 | // Check the function attributes and profiles to find out if this function |
| 9337 | // should be optimized for size. |
| 9338 | ScalarEpilogueLowering SEL = getScalarEpilogueLowering( |
| 9339 | F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, LVL); |
| 9340 | |
| 9341 | // Entrance to the VPlan-native vectorization path. Outer loops are processed |
| 9342 | // here. They may require CFG and instruction level transformations before |
| 9343 | // even evaluating whether vectorization is profitable. Since we cannot modify |
| 9344 | // the incoming IR, we need to build VPlan upfront in the vectorization |
| 9345 | // pipeline. |
| 9346 | if (!L->isInnermost()) |
| 9347 | return processLoopInVPlanNativePath(L, PSE, LI, DT, &LVL, TTI, TLI, DB, AC, |
| 9348 | ORE, BFI, PSI, Hints); |
| 9349 | |
| 9350 | assert(L->isInnermost() && "Inner loop expected." ); |
| 9351 | |
| 9352 | // Check the loop for a trip count threshold: vectorize loops with a tiny trip |
| 9353 | // count by optimizing for size, to minimize overheads. |
| 9354 | auto ExpectedTC = getSmallBestKnownTC(*SE, L); |
| 9355 | if (ExpectedTC && *ExpectedTC < TinyTripCountVectorThreshold) { |
| 9356 | LLVM_DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " |
| 9357 | << "This loop is worth vectorizing only if no scalar " |
| 9358 | << "iteration overheads are incurred." ); |
| 9359 | if (Hints.getForce() == LoopVectorizeHints::FK_Enabled) |
| 9360 | LLVM_DEBUG(dbgs() << " But vectorizing was explicitly forced.\n" ); |
| 9361 | else { |
| 9362 | LLVM_DEBUG(dbgs() << "\n" ); |
| 9363 | SEL = CM_ScalarEpilogueNotAllowedLowTripLoop; |
| 9364 | } |
| 9365 | } |
| 9366 | |
| 9367 | // Check the function attributes to see if implicit floats are allowed. |
| 9368 | // FIXME: This check doesn't seem possibly correct -- what if the loop is |
| 9369 | // an integer loop and the vector instructions selected are purely integer |
| 9370 | // vector instructions? |
| 9371 | if (F->hasFnAttribute(Attribute::NoImplicitFloat)) { |
| 9372 | reportVectorizationFailure( |
| 9373 | "Can't vectorize when the NoImplicitFloat attribute is used" , |
| 9374 | "loop not vectorized due to NoImplicitFloat attribute" , |
| 9375 | "NoImplicitFloat" , ORE, L); |
| 9376 | Hints.emitRemarkWithHints(); |
| 9377 | return false; |
| 9378 | } |
| 9379 | |
| 9380 | // Check if the target supports potentially unsafe FP vectorization. |
| 9381 | // FIXME: Add a check for the type of safety issue (denormal, signaling) |
| 9382 | // for the target we're vectorizing for, to make sure none of the |
| 9383 | // additional fp-math flags can help. |
| 9384 | if (Hints.isPotentiallyUnsafe() && |
| 9385 | TTI->isFPVectorizationPotentiallyUnsafe()) { |
| 9386 | reportVectorizationFailure( |
| 9387 | "Potentially unsafe FP op prevents vectorization" , |
| 9388 | "loop not vectorized due to unsafe FP support." , |
| 9389 | "UnsafeFP" , ORE, L); |
| 9390 | Hints.emitRemarkWithHints(); |
| 9391 | return false; |
| 9392 | } |
| 9393 | |
| 9394 | bool UseInterleaved = TTI->enableInterleavedAccessVectorization(); |
| 9395 | InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL.getLAI()); |
| 9396 | |
| 9397 | // If an override option has been passed in for interleaved accesses, use it. |
| 9398 | if (EnableInterleavedMemAccesses.getNumOccurrences() > 0) |
| 9399 | UseInterleaved = EnableInterleavedMemAccesses; |
| 9400 | |
| 9401 | // Analyze interleaved memory accesses. |
| 9402 | if (UseInterleaved) { |
| 9403 | IAI.analyzeInterleaving(useMaskedInterleavedAccesses(*TTI)); |
| 9404 | } |
| 9405 | |
| 9406 | // Use the cost model. |
| 9407 | LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE, |
| 9408 | F, &Hints, IAI); |
| 9409 | CM.collectValuesToIgnore(); |
| 9410 | |
| 9411 | // Use the planner for vectorization. |
| 9412 | LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM, IAI, PSE); |
| 9413 | |
| 9414 | // Get user vectorization factor and interleave count. |
| 9415 | ElementCount UserVF = Hints.getWidth(); |
| 9416 | unsigned UserIC = Hints.getInterleave(); |
| 9417 | |
| 9418 | // Plan how to best vectorize, return the best VF and its cost. |
| 9419 | Optional<VectorizationFactor> MaybeVF = LVP.plan(UserVF, UserIC); |
| 9420 | |
| 9421 | VectorizationFactor VF = VectorizationFactor::Disabled(); |
| 9422 | unsigned IC = 1; |
| 9423 | |
| 9424 | if (MaybeVF) { |
| 9425 | VF = *MaybeVF; |
| 9426 | // Select the interleave count. |
| 9427 | IC = CM.selectInterleaveCount(VF.Width, VF.Cost); |
| 9428 | } |
| 9429 | |
| 9430 | // Identify the diagnostic messages that should be produced. |
| 9431 | std::pair<StringRef, std::string> VecDiagMsg, IntDiagMsg; |
| 9432 | bool VectorizeLoop = true, InterleaveLoop = true; |
| 9433 | if (Requirements.doesNotMeet(F, L, Hints)) { |
| 9434 | LLVM_DEBUG(dbgs() << "LV: Not vectorizing: loop did not meet vectorization " |
| 9435 | "requirements.\n" ); |
| 9436 | Hints.emitRemarkWithHints(); |
| 9437 | return false; |
| 9438 | } |
| 9439 | |
| 9440 | if (VF.Width.isScalar()) { |
| 9441 | LLVM_DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n" ); |
| 9442 | VecDiagMsg = std::make_pair( |
| 9443 | "VectorizationNotBeneficial" , |
| 9444 | "the cost-model indicates that vectorization is not beneficial" ); |
| 9445 | VectorizeLoop = false; |
| 9446 | } |
| 9447 | |
| 9448 | if (!MaybeVF && UserIC > 1) { |
| 9449 | // Tell the user interleaving was avoided up-front, despite being explicitly |
| 9450 | // requested. |
| 9451 | LLVM_DEBUG(dbgs() << "LV: Ignoring UserIC, because vectorization and " |
| 9452 | "interleaving should be avoided up front\n" ); |
| 9453 | IntDiagMsg = std::make_pair( |
| 9454 | "InterleavingAvoided" , |
| 9455 | "Ignoring UserIC, because interleaving was avoided up front" ); |
| 9456 | InterleaveLoop = false; |
| 9457 | } else if (IC == 1 && UserIC <= 1) { |
| 9458 | // Tell the user interleaving is not beneficial. |
| 9459 | LLVM_DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n" ); |
| 9460 | IntDiagMsg = std::make_pair( |
| 9461 | "InterleavingNotBeneficial" , |
| 9462 | "the cost-model indicates that interleaving is not beneficial" ); |
| 9463 | InterleaveLoop = false; |
| 9464 | if (UserIC == 1) { |
| 9465 | IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled" ; |
| 9466 | IntDiagMsg.second += |
| 9467 | " and is explicitly disabled or interleave count is set to 1" ; |
| 9468 | } |
| 9469 | } else if (IC > 1 && UserIC == 1) { |
| 9470 | // Tell the user interleaving is beneficial, but it explicitly disabled. |
| 9471 | LLVM_DEBUG( |
| 9472 | dbgs() << "LV: Interleaving is beneficial but is explicitly disabled." ); |
| 9473 | IntDiagMsg = std::make_pair( |
| 9474 | "InterleavingBeneficialButDisabled" , |
| 9475 | "the cost-model indicates that interleaving is beneficial " |
| 9476 | "but is explicitly disabled or interleave count is set to 1" ); |
| 9477 | InterleaveLoop = false; |
| 9478 | } |
| 9479 | |
| 9480 | // Override IC if user provided an interleave count. |
| 9481 | IC = UserIC > 0 ? UserIC : IC; |
| 9482 | |
| 9483 | // Emit diagnostic messages, if any. |
| 9484 | const char *VAPassName = Hints.vectorizeAnalysisPassName(); |
| 9485 | if (!VectorizeLoop && !InterleaveLoop) { |
| 9486 | // Do not vectorize or interleaving the loop. |
| 9487 | ORE->emit([&]() { |
| 9488 | return OptimizationRemarkMissed(VAPassName, VecDiagMsg.first, |
| 9489 | L->getStartLoc(), L->getHeader()) |
| 9490 | << VecDiagMsg.second; |
| 9491 | }); |
| 9492 | ORE->emit([&]() { |
| 9493 | return OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first, |
| 9494 | L->getStartLoc(), L->getHeader()) |
| 9495 | << IntDiagMsg.second; |
| 9496 | }); |
| 9497 | return false; |
| 9498 | } else if (!VectorizeLoop && InterleaveLoop) { |
| 9499 | LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n'); |
| 9500 | ORE->emit([&]() { |
| 9501 | return OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first, |
| 9502 | L->getStartLoc(), L->getHeader()) |
| 9503 | << VecDiagMsg.second; |
| 9504 | }); |
| 9505 | } else if (VectorizeLoop && !InterleaveLoop) { |
| 9506 | LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width |
| 9507 | << ") in " << DebugLocStr << '\n'); |
| 9508 | ORE->emit([&]() { |
| 9509 | return OptimizationRemarkAnalysis(LV_NAME, IntDiagMsg.first, |
| 9510 | L->getStartLoc(), L->getHeader()) |
| 9511 | << IntDiagMsg.second; |
| 9512 | }); |
| 9513 | } else if (VectorizeLoop && InterleaveLoop) { |
| 9514 | LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width |
| 9515 | << ") in " << DebugLocStr << '\n'); |
| 9516 | LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n'); |
| 9517 | } |
| 9518 | |
| 9519 | LVP.setBestPlan(VF.Width, IC); |
| 9520 | |
| 9521 | using namespace ore; |
| 9522 | bool DisableRuntimeUnroll = false; |
| 9523 | MDNode *OrigLoopID = L->getLoopID(); |
| 9524 | |
| 9525 | if (!VectorizeLoop) { |
| 9526 | assert(IC > 1 && "interleave count should not be 1 or 0" ); |
| 9527 | // If we decided that it is not legal to vectorize the loop, then |
| 9528 | // interleave it. |
| 9529 | InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL, &CM, |
| 9530 | BFI, PSI); |
| 9531 | LVP.executePlan(Unroller, DT); |
| 9532 | |
| 9533 | ORE->emit([&]() { |
| 9534 | return OptimizationRemark(LV_NAME, "Interleaved" , L->getStartLoc(), |
| 9535 | L->getHeader()) |
| 9536 | << "interleaved loop (interleaved count: " |
| 9537 | << NV("InterleaveCount" , IC) << ")" ; |
| 9538 | }); |
| 9539 | } else { |
| 9540 | // If we decided that it is *legal* to vectorize the loop, then do it. |
| 9541 | |
| 9542 | // Consider vectorizing the epilogue too if it's profitable. |
| 9543 | VectorizationFactor EpilogueVF = |
| 9544 | CM.selectEpilogueVectorizationFactor(VF.Width, LVP); |
| 9545 | if (EpilogueVF.Width.isVector()) { |
| 9546 | |
| 9547 | // The first pass vectorizes the main loop and creates a scalar epilogue |
| 9548 | // to be vectorized by executing the plan (potentially with a different |
| 9549 | // factor) again shortly afterwards. |
| 9550 | EpilogueLoopVectorizationInfo EPI(VF.Width.getKnownMinValue(), IC, |
| 9551 | EpilogueVF.Width.getKnownMinValue(), 1); |
| 9552 | EpilogueVectorizerMainLoop MainILV(L, PSE, LI, DT, TLI, TTI, AC, ORE, EPI, |
| 9553 | &LVL, &CM, BFI, PSI); |
| 9554 | |
| 9555 | LVP.setBestPlan(EPI.MainLoopVF, EPI.MainLoopUF); |
| 9556 | LVP.executePlan(MainILV, DT); |
| 9557 | ++LoopsVectorized; |
| 9558 | |
| 9559 | simplifyLoop(L, DT, LI, SE, AC, nullptr, false /* PreserveLCSSA */); |
| 9560 | formLCSSARecursively(*L, *DT, LI, SE); |
| 9561 | |
| 9562 | // Second pass vectorizes the epilogue and adjusts the control flow |
| 9563 | // edges from the first pass. |
| 9564 | LVP.setBestPlan(EPI.EpilogueVF, EPI.EpilogueUF); |
| 9565 | EPI.MainLoopVF = EPI.EpilogueVF; |
| 9566 | EPI.MainLoopUF = EPI.EpilogueUF; |
| 9567 | EpilogueVectorizerEpilogueLoop EpilogILV(L, PSE, LI, DT, TLI, TTI, AC, |
| 9568 | ORE, EPI, &LVL, &CM, BFI, PSI); |
| 9569 | LVP.executePlan(EpilogILV, DT); |
| 9570 | ++LoopsEpilogueVectorized; |
| 9571 | |
| 9572 | if (!MainILV.areSafetyChecksAdded()) |
| 9573 | DisableRuntimeUnroll = true; |
| 9574 | } else { |
| 9575 | InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, IC, |
| 9576 | &LVL, &CM, BFI, PSI); |
| 9577 | LVP.executePlan(LB, DT); |
| 9578 | ++LoopsVectorized; |
| 9579 | |
| 9580 | // Add metadata to disable runtime unrolling a scalar loop when there are |
| 9581 | // no runtime checks about strides and memory. A scalar loop that is |
| 9582 | // rarely used is not worth unrolling. |
| 9583 | if (!LB.areSafetyChecksAdded()) |
| 9584 | DisableRuntimeUnroll = true; |
| 9585 | } |
| 9586 | |
| 9587 | // Report the vectorization decision. |
| 9588 | ORE->emit([&]() { |
| 9589 | return OptimizationRemark(LV_NAME, "Vectorized" , L->getStartLoc(), |
| 9590 | L->getHeader()) |
| 9591 | << "vectorized loop (vectorization width: " |
| 9592 | << NV("VectorizationFactor" , VF.Width) |
| 9593 | << ", interleaved count: " << NV("InterleaveCount" , IC) << ")" ; |
| 9594 | }); |
| 9595 | } |
| 9596 | |
| 9597 | Optional<MDNode *> RemainderLoopID = |
| 9598 | makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, |
| 9599 | LLVMLoopVectorizeFollowupEpilogue}); |
| 9600 | if (RemainderLoopID.hasValue()) { |
| 9601 | L->setLoopID(RemainderLoopID.getValue()); |
| 9602 | } else { |
| 9603 | if (DisableRuntimeUnroll) |
| 9604 | AddRuntimeUnrollDisableMetaData(L); |
| 9605 | |
| 9606 | // Mark the loop as already vectorized to avoid vectorizing again. |
| 9607 | Hints.setAlreadyVectorized(); |
| 9608 | } |
| 9609 | |
| 9610 | assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs())); |
| 9611 | return true; |
| 9612 | } |
| 9613 | |
| 9614 | LoopVectorizeResult LoopVectorizePass::runImpl( |
| 9615 | Function &F, ScalarEvolution &SE_, LoopInfo &LI_, TargetTransformInfo &TTI_, |
| 9616 | DominatorTree &DT_, BlockFrequencyInfo &BFI_, TargetLibraryInfo *TLI_, |
| 9617 | DemandedBits &DB_, AAResults &AA_, AssumptionCache &AC_, |
| 9618 | std::function<const LoopAccessInfo &(Loop &)> &GetLAA_, |
| 9619 | OptimizationRemarkEmitter &ORE_, ProfileSummaryInfo *PSI_) { |
| 9620 | SE = &SE_; |
| 9621 | LI = &LI_; |
| 9622 | TTI = &TTI_; |
| 9623 | DT = &DT_; |
| 9624 | BFI = &BFI_; |
| 9625 | TLI = TLI_; |
| 9626 | AA = &AA_; |
| 9627 | AC = &AC_; |
| 9628 | GetLAA = &GetLAA_; |
| 9629 | DB = &DB_; |
| 9630 | ORE = &ORE_; |
| 9631 | PSI = PSI_; |
| 9632 | |
| 9633 | // Don't attempt if |
| 9634 | // 1. the target claims to have no vector registers, and |
| 9635 | // 2. interleaving won't help ILP. |
| 9636 | // |
| 9637 | // The second condition is necessary because, even if the target has no |
| 9638 | // vector registers, loop vectorization may still enable scalar |
| 9639 | // interleaving. |
| 9640 | if (!TTI->getNumberOfRegisters(TTI->getRegisterClassForType(true)) && |
| 9641 | TTI->getMaxInterleaveFactor(1) < 2) |
| 9642 | return LoopVectorizeResult(false, false); |
| 9643 | |
| 9644 | bool Changed = false, CFGChanged = false; |
| 9645 | |
| 9646 | // The vectorizer requires loops to be in simplified form. |
| 9647 | // Since simplification may add new inner loops, it has to run before the |
| 9648 | // legality and profitability checks. This means running the loop vectorizer |
| 9649 | // will simplify all loops, regardless of whether anything end up being |
| 9650 | // vectorized. |
| 9651 | for (auto &L : *LI) |
| 9652 | Changed |= CFGChanged |= |
| 9653 | simplifyLoop(L, DT, LI, SE, AC, nullptr, false /* PreserveLCSSA */); |
| 9654 | |
| 9655 | // Build up a worklist of inner-loops to vectorize. This is necessary as |
| 9656 | // the act of vectorizing or partially unrolling a loop creates new loops |
| 9657 | // and can invalidate iterators across the loops. |
| 9658 | SmallVector<Loop *, 8> Worklist; |
| 9659 | |
| 9660 | for (Loop *L : *LI) |
| 9661 | collectSupportedLoops(*L, LI, ORE, Worklist); |
| 9662 | |
| 9663 | LoopsAnalyzed += Worklist.size(); |
| 9664 | |
| 9665 | // Now walk the identified inner loops. |
| 9666 | while (!Worklist.empty()) { |
| 9667 | Loop *L = Worklist.pop_back_val(); |
| 9668 | |
| 9669 | // For the inner loops we actually process, form LCSSA to simplify the |
| 9670 | // transform. |
| 9671 | Changed |= formLCSSARecursively(*L, *DT, LI, SE); |
| 9672 | |
| 9673 | Changed |= CFGChanged |= processLoop(L); |
| 9674 | } |
| 9675 | |
| 9676 | // Process each loop nest in the function. |
| 9677 | return LoopVectorizeResult(Changed, CFGChanged); |
| 9678 | } |
| 9679 | |
| 9680 | PreservedAnalyses LoopVectorizePass::run(Function &F, |
| 9681 | FunctionAnalysisManager &AM) { |
| 9682 | auto &SE = AM.getResult<ScalarEvolutionAnalysis>(F); |
| 9683 | auto &LI = AM.getResult<LoopAnalysis>(F); |
| 9684 | auto &TTI = AM.getResult<TargetIRAnalysis>(F); |
| 9685 | auto &DT = AM.getResult<DominatorTreeAnalysis>(F); |
| 9686 | auto &BFI = AM.getResult<BlockFrequencyAnalysis>(F); |
| 9687 | auto &TLI = AM.getResult<TargetLibraryAnalysis>(F); |
| 9688 | auto &AA = AM.getResult<AAManager>(F); |
| 9689 | auto &AC = AM.getResult<AssumptionAnalysis>(F); |
| 9690 | auto &DB = AM.getResult<DemandedBitsAnalysis>(F); |
| 9691 | auto &ORE = AM.getResult<OptimizationRemarkEmitterAnalysis>(F); |
| 9692 | MemorySSA *MSSA = EnableMSSALoopDependency |
| 9693 | ? &AM.getResult<MemorySSAAnalysis>(F).getMSSA() |
| 9694 | : nullptr; |
| 9695 | |
| 9696 | auto &LAM = AM.getResult<LoopAnalysisManagerFunctionProxy>(F).getManager(); |
| 9697 | std::function<const LoopAccessInfo &(Loop &)> GetLAA = |
| 9698 | [&](Loop &L) -> const LoopAccessInfo & { |
| 9699 | LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE, |
| 9700 | TLI, TTI, nullptr, MSSA}; |
| 9701 | return LAM.getResult<LoopAccessAnalysis>(L, AR); |
| 9702 | }; |
| 9703 | auto &MAMProxy = AM.getResult<ModuleAnalysisManagerFunctionProxy>(F); |
| 9704 | ProfileSummaryInfo *PSI = |
| 9705 | MAMProxy.getCachedResult<ProfileSummaryAnalysis>(*F.getParent()); |
| 9706 | LoopVectorizeResult Result = |
| 9707 | runImpl(F, SE, LI, TTI, DT, BFI, &TLI, DB, AA, AC, GetLAA, ORE, PSI); |
| 9708 | if (!Result.MadeAnyChange) |
| 9709 | return PreservedAnalyses::all(); |
| 9710 | PreservedAnalyses PA; |
| 9711 | |
| 9712 | // We currently do not preserve loopinfo/dominator analyses with outer loop |
| 9713 | // vectorization. Until this is addressed, mark these analyses as preserved |
| 9714 | // only for non-VPlan-native path. |
| 9715 | // TODO: Preserve Loop and Dominator analyses for VPlan-native path. |
| 9716 | if (!EnableVPlanNativePath) { |
| 9717 | PA.preserve<LoopAnalysis>(); |
| 9718 | PA.preserve<DominatorTreeAnalysis>(); |
| 9719 | } |
| 9720 | PA.preserve<BasicAA>(); |
| 9721 | PA.preserve<GlobalsAA>(); |
| 9722 | if (!Result.MadeCFGChange) |
| 9723 | PA.preserveSet<CFGAnalyses>(); |
| 9724 | return PA; |
| 9725 | } |
| 9726 | |